{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification\n",
    "\n",
    "## Overview\n",
    "In classification problems, the output space consists of a set of $C$ labels, which are referred to as `classes`. These labels form a set denoted as $\\mathcal{Y} = \\{1, 2, \\ldots, C\\}$. The goal in such problem is to predict the correct label for a given input, a task widely known as `pattern recognition`. \n",
    "\n",
    "In cases where there are only two possible classes, the labels are typically represented as $y \\in \\{0, 1\\}$ or  $y \\in \\{-1, +1\\}$. This specific type of classification is called binary classification.\n",
    "\n",
    "\n",
    "## Iris Flowers\n",
    "\n",
    "As an example of a classification task, consider classifying Iris flowers into one of three subspecies: Setosa, Versicolor, and Virginica. The image below illustrates an example from each class.\n",
    "\n",
    "```{image} ./figures/iris.png\n",
    ":width: 680\n",
    ":align: center\n",
    "```\n",
    "Three types of Iris flowers: Setosa (left), Versicolor (center), and Virginica (right).\n",
    "<br>\n",
    "\n",
    "The features in the Iris dataset are: sepal length, sepal width, petal length, and petal width. These features are used to classify the flowers into one of three subspecies: Setosa, Versicolor, or Virginica.\n",
    "\n",
    "The following code demonstrates how to load the Iris dataset using the sklearn library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.utils._bunch.Bunch'>\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "# Load the Iris dataset\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "print(type(iris))  # Print the type of the dataset object\n",
    "print(iris.feature_names)  # Print the names of the dataset's features\n",
    "print(iris.target_names)  # Print the names of the target classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Iris dataset is a collection of 150 labeled examples of Iris flowers, 50 of each type, described by these 4 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Extract feature data (X) and target labels (y) from the Iris dataset\n",
    "# Features: Sepal length, sepal width, petal length, petal width\n",
    "X = iris.data\n",
    "# Target labels: Encoded as integers (0 = Setosa, 1 = Versicolor, 2 = Virginica)\n",
    "y = iris.target\n",
    "\n",
    "# Convert the feature data and target labels into a Pandas DataFrame\n",
    "df = pd.DataFrame(\n",
    "    data=X, columns=iris.feature_names\n",
    ")  # Create a DataFrame with feature names as column headers\n",
    "\n",
    "# Display the first few rows of the DataFrame to verify its structure and content\n",
    "df.head()  # Returns the first 5 rows, including features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "    label  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column for human-readable class labels using the target names\n",
    "# Map the numerical target labels (0, 1, 2) to their corresponding class names (Setosa, Versicolor, Virginica)\n",
    "df[\"label\"] = pd.Series(iris.target_names[y], dtype=\"category\")\n",
    "\n",
    "# Display the first few rows of the DataFrame to verify its structure and content\n",
    "df.head()  # Returns the first 5 rows, including features and their corresponding class labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For tabular data with a small number of features, it is common to make a `pair plot`, in which panel $(i, j)$ shows a scatter plot of variables $i$ and $j$, and the diagonal entries $(i,i)$ show the marginal density of variable $i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1117.75x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define a custom color palette to match the colors used in decision tree visualizations\n",
    "# The keys are the class names (labels), and the values are the colors assigned to each class\n",
    "palette = {\n",
    "    \"setosa\": \"orange\",  # Setosa class will be represented in orange\n",
    "    \"versicolor\": \"green\",  # Versicolor class will be represented in green\n",
    "    \"virginica\": \"purple\",  # Virginica class will be represented in purple\n",
    "}\n",
    "\n",
    "# Create a pair plot using Seaborn to visualize pairwise relationships between features\n",
    "# - `df`: The DataFrame containing the Iris dataset\n",
    "# - `vars`: Specifies the columns to use for the pair plot; in this case, the first 4 feature columns\n",
    "# - `hue`: Groups data points by the \"label\" column, which corresponds to the class labels\n",
    "# - `palette`: Applies the custom color mapping defined above for the classes\n",
    "g = sns.pairplot(df, vars=df.columns[0:4], hue=\"label\", palette=palette)\n",
    "\n",
    "# Display the resulting plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above demonstrates that Iris setosa is easily distinguishable due to its unique feature patterns. However, classifying Iris versicolor and Iris virginica is more difficult because their feature spaces overlap.\n",
    "\n",
    "## Standardization\n",
    "\n",
    "`Standardization` is a preprocessing technique that rescales the features so that they have the properties of a `standard normal distribution with a mean of 0 and a standard deviation of 1`. This is important in machine learning because it ensures that the features are on a similar scale, preventing some features from dominating the learning process simply because they have larger magnitudes.\n",
    "\n",
    "The `StandardScaler` in scikit-learn works by calculating the mean and standard deviation of each feature in the training set and then transforming the data based on these statistics. The formula for standardization is:\n",
    "\n",
    "$$ Standardized Value = \\frac{Original Value − Mean}{ Standard Deviation}$$\n",
    "\n",
    "The purpose of standardization is to make the features of the dataset comparable and to ensure that they all contribute equally to the model training. It is particularly important when working with algorithms that are sensitive to the scale of the input features, such as gradient-based optimization algorithms used in neural networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.019004</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.328414</td>\n",
       "      <td>-1.397064</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.098217</td>\n",
       "      <td>-1.283389</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.249201</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0          -0.900681          1.019004          -1.340227         -1.315444   \n",
       "1          -1.143017         -0.131979          -1.340227         -1.315444   \n",
       "2          -1.385353          0.328414          -1.397064         -1.315444   \n",
       "3          -1.506521          0.098217          -1.283389         -1.315444   \n",
       "4          -1.021849          1.249201          -1.340227         -1.315444   \n",
       "\n",
       "    label  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Convert to pandas dataframe\n",
    "df_scaled = pd.DataFrame(data=X_scaled, columns=iris.feature_names)\n",
    "df_scaled[\"label\"] = pd.Series(iris.target_names[y], dtype=\"category\")\n",
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting Dataset\n",
    "To evaluate a classification model effectively, we divide the dataset into two subsets:\n",
    "\n",
    "- **Training Set**: Used to train the model.\n",
    "- **Testing (or Validation) Set**: Used to evaluate the model's performance on unseen data.\n",
    "\n",
    "This ensures that the model's performance is measured accurately and prevents overfitting. We’ll use the `train_test_split` function from `scikit-learn` to achieve this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 120 samples\n",
      "Testing set size: 30 samples\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split  # For splitting the dataset\n",
    "from sklearn.naive_bayes import GaussianNB  # A classification algorithm (Naive Bayes)\n",
    "from sklearn.metrics import accuracy_score  # To measure the model's performance\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "# - X: Feature matrix (sepal/petal dimensions for each Iris sample)\n",
    "# - y: Target labels (numerical representation of Iris species)\n",
    "# - test_size=0.2: 20% of the data is reserved for testing, and 80% for training\n",
    "# - random_state=42: Ensures reproducibility by using a fixed seed for randomness\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_scaled, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "# The train_test_split function performs:\n",
    "# - Random shuffling of the dataset.\n",
    "# - Division of data into two parts: training set (X_train, y_train) and testing set (X_test, y_test).\n",
    "# - A specified proportion for the split (e.g., 80% training, 20% testing).\n",
    "\n",
    "# Print the shapes of the resulting datasets for verification\n",
    "print(f\"Training set size: {X_train.shape[0]} samples\")\n",
    "print(f\"Testing set size: {X_test.shape[0]} samples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes Classifier\n",
    "\n",
    "The Naive Bayes method is a probabilistic classifier based on Bayes' Theorem. It assumes that features are independent given the class label. Since the Iris dataset consists of continuous, real-valued features, we use the `Gaussian` Naive Bayes classifier, which assumes the feature values are normally distributed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.00%\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.naive_bayes import GaussianNB  # Gaussian Naive Bayes classifier\n",
    "from sklearn.metrics import accuracy_score  # To evaluate model accuracy\n",
    "\n",
    "# Train a Gaussian Naive Bayes classifier\n",
    "nb_classifier = GaussianNB()  # Initialize the Gaussian Naive Bayes classifier\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "# - X_train: Feature matrix for training\n",
    "# - y_train: Target labels for training\n",
    "nb_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the testing set\n",
    "# - X_test: Feature matrix for testing\n",
    "# - y_test: Target labels for testing\n",
    "y_pred = nb_classifier.predict(X_test)  # Predict the class labels for the test set\n",
    "\n",
    "# Calculate the accuracy of the model\n",
    "accuracy = accuracy_score(y_test, y_pred)  # Proportion of correct predictions\n",
    "print(f\"Accuracy: {accuracy * 100:.2f}%\")  # Print accuracy as a percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We’ll train the Gaussian Naive Bayes classifier on the Iris dataset, evaluate its performance using the training and testing sets, and then use 5-fold cross-validation to assess the model's accuracy across different splits of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.93333333 0.96666667 0.93333333 0.93333333 1.        ]\n",
      "Mean accuracy: 0.9533333333333334\n"
     ]
    }
   ],
   "source": [
    "# Create a Naive Bayes classifier (Gaussian Naive Bayes for this example)\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Perform 5-fold cross-validation to evaluate the model's performance\n",
    "# - `cv=5`: Specifies 5-fold cross-validation\n",
    "cv_scores = cross_val_score(nb_classifier, X_scaled, y, cv=5)\n",
    "\n",
    "# Print cross-validation scores for each fold\n",
    "print(\"Cross-validation scores:\", cv_scores)\n",
    "\n",
    "# Calculate and print the mean accuracy from cross-validation\n",
    "mean_accuracy = np.mean(cv_scores)  # Average accuracy across all folds\n",
    "print(\"Mean accuracy:\", mean_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine (SVM) Classifier\n",
    "\n",
    "Support Vector Machines (SVMs) are powerful classification models that find the optimal **hyperplane** to separate data points in a feature space. SVMs are particularly effective for binary classification, but they can also handle multi-class problems (like the Iris dataset) using extensions like one-vs-one or one-vs-rest strategies.Let's try the Naive Bayes method to classify the Iris flowers. \n",
    "\n",
    "### Understanding Kernels in SVMs\n",
    "Kernels are mathematical functions that transform data into a higher-dimensional space where a linear hyperplane can separate classes. The choice of kernel plays a critical role in the SVM's ability to handle different types of data:\n",
    "\n",
    "- Linear Kernel: Suitable for linearly separable data. It finds a straight hyperplane to separate the classes.\n",
    "- Polynomial Kernel: Useful when the data requires a polynomial decision boundary.\n",
    "- Radial Basis Function (RBF) Kernel (default): Often a good choice for non-linear data as it maps data to an infinite-dimensional space.\n",
    "- Sigmoid Kernel: Can handle sigmoid-like data distributions but is less commonly used.\n",
    "\n",
    "The choice of kernel depends on the nature of the data and the problem we are trying to solve. It's often a good idea to experiment with different kernels to find the one that works best for your specific dataset.\n",
    "\n",
    "For this example, we use the linear kernel, as it is computationally efficient and works well with the Iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.67%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC  # Support Vector Classifier (SVM implementation)\n",
    "from sklearn.metrics import accuracy_score  # For evaluating the model's accuracy\n",
    "\n",
    "# Initialize the SVM classifier\n",
    "# - `kernel=\"linear\"`: Specifies that we are using a linear kernel for this example\n",
    "svm_classifier_linear = SVC(kernel=\"linear\")\n",
    "\n",
    "# Train the SVM model on the training data\n",
    "# - `X_train`: Feature matrix for training\n",
    "# - `y_train`: Target labels for training\n",
    "svm_classifier_linear.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the trained model on the testing set\n",
    "# - `X_test`: Feature matrix for testing\n",
    "# - `y_test`: True target labels for testing\n",
    "y_pred = svm_classifier_linear.predict(X_test)  # Predict class labels for the test set\n",
    "\n",
    "# Calculate the model's accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)  # Proportion of correct predictions\n",
    "print(f\"Accuracy: {accuracy * 100:.2f}%\")  # Print accuracy as a percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using 5-Fold Cross-Validation\n",
    "\n",
    "We will use 5-fold cross-validation to evaluate the accuracy of our SVM classifier. This approach divides our dataset into five subsets, trains the model on four subsets, and tests it on the remaining subset, repeating this process five times. This method helps us get a reliable estimate of the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.96666667 0.96666667 0.96666667 0.96666667 1.        ]\n",
      "Mean accuracy: 0.9733333333333334\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "# Define the cross-validation method. We are using 5-fold cross-validation.\n",
    "# - `n_splits=5` specifies the number of folds.\n",
    "# - `shuffle=True` ensures that the data is shuffled before splitting into folds, which helps in achieving better generalization.\n",
    "# - `random_state=42` sets a fixed random seed for reproducibility, so that we get the same data splits every time we run the code.\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# Perform 5-fold cross-validation\n",
    "# - `svm_classifier` is our model (assumed to be predefined).\n",
    "# - `X` is the feature matrix (assumed to be predefined).\n",
    "# - `y` is the target vector (assumed to be predefined).\n",
    "# - `cv=kf` uses the KFold object we defined as the cross-validation strategy.\n",
    "cv_scores = cross_val_score(svm_classifier_linear, X_scaled, y, cv=kf)\n",
    "\n",
    "# Step 4: Print the cross-validation scores for each fold\n",
    "print(\"Cross-validation scores:\", cv_scores)\n",
    "\n",
    "# Step 4: Calculate and print the mean accuracy\n",
    "# - We use `np.mean` to compute the average of the cross-validation scores.\n",
    "mean_accuracy = np.mean(cv_scores)\n",
    "print(\"Mean accuracy:\", mean_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to evaluate the performance of the default kernel, Radial Basis Function (RBF), for a Support Vector Classifier (SVC) using 5-fold cross-validation on the Iris dataset and compare it with the linear kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [1.         0.96666667 0.96666667 0.93333333 0.96666667]\n",
      "Mean accuracy: 0.9666666666666668\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "\n",
    "# Define the cross-validation method using 5-fold cross-validation\n",
    "# - We reuse `kf` from the previous code block for consistency.\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# Create an instance of the classifier\n",
    "# - `SVC()` creates a Support Vector Classifier with the default RBF kernel.\n",
    "svm_classifier_rbf = SVC()\n",
    "\n",
    "# Perform cross-validation and calculate the scores\n",
    "# - `classifier` is the model we want to evaluate.\n",
    "# - `X` is the feature matrix (assumed to be predefined).\n",
    "# - `y` is the target vector (assumed to be predefined).\n",
    "# - `cv=kf` uses the KFold object we defined as the cross-validation strategy.\n",
    "# - `scoring=\"accuracy\"` specifies that we want to evaluate the model based on accuracy.\n",
    "scores = cross_val_score(svm_classifier_rbf, X_scaled, y, cv=kf, scoring=\"accuracy\")\n",
    "\n",
    "# Print the cross-validation scores for each fold\n",
    "print(\"Cross-validation scores:\", scores)\n",
    "\n",
    "# Calculate and print the mean accuracy\n",
    "# - We use `np.mean` to compute the average of the cross-validation scores.\n",
    "mean_accuracy = np.mean(scores)\n",
    "print(\"Mean accuracy:\", mean_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this Iris dataset, the linear kernel works better than the RBF kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Networks\n",
    "\n",
    "`MLPClassifier` stands for Multi-Layer Perceptron Classifier. It is a type of artificial neural network-based classification algorithm. The term \"multi-layer perceptron\" refers to the architecture of the network, which consists of multiple layers of nodes (neurons) organized in a feedforward manner.\n",
    "\n",
    "The MLPClassifier in scikit-learn has several important parameters that allow you to customize the architecture and behavior of the neural network. Here are some key parameters:\n",
    "\n",
    "- hidden_layer_sizes (default=(100,)): This parameter defines the architecture of the neural network. It is a tuple where each element represents the number of neurons in the corresponding hidden layer. For example, hidden_layer_sizes=(10, 5) defines a network with two hidden layers, the first with 10 neurons and the second with 5.\n",
    "\n",
    "- activation (default='relu'): Activation function for the hidden layers. Common choices include 'relu' (Rectified Linear Unit), 'logistic' (sigmoid), and 'tanh' (hyperbolic tangent).\n",
    "\n",
    "- solver (default='adam'): Optimization algorithm to use. Common choices include 'sgd' (stochastic gradient descent), 'adam' (a popular variant of gradient descent), and 'lbfgs' (a quasi-Newton method).\n",
    "\n",
    "- learning_rate_init (default=0.001): The initial learning rate. It controls the step size in updating the weights.\n",
    "\n",
    "- max_iter (default=200): Maximum number of iterations. The solver iterates until convergence (determined by the tol parameter) or until this number of iterations is reached.\n",
    "\n",
    "- random_state (default=None): Seed used by the random number generator.\n",
    "\n",
    "- tol (default=1e-4): Tolerance for the optimization. If the change in the objective function is smaller than this value, the optimization will be considered as converged.\n",
    "\n",
    "- verbose: If set to True, it prints progress messages to the console during training.\n",
    "\n",
    "These are just some of the key parameters. Depending on your specific use case, you may also want to explore other parameters provided by the MLPClassifier class. It's often beneficial to experiment with different parameter values and architectures to find the combination that works best for your particular dataset and problem.\n",
    "\n",
    "Ref: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.36714411\n",
      "Iteration 2, loss = 1.18921901\n",
      "Iteration 3, loss = 1.00875630\n",
      "Iteration 4, loss = 0.85367491\n",
      "Iteration 5, loss = 0.72641469\n",
      "Iteration 6, loss = 0.62117297\n",
      "Iteration 7, loss = 0.53514402\n",
      "Iteration 8, loss = 0.46618582\n",
      "Iteration 9, loss = 0.41118992\n",
      "Iteration 10, loss = 0.36897772\n",
      "Iteration 11, loss = 0.33749092\n",
      "Iteration 12, loss = 0.31376203\n",
      "Iteration 13, loss = 0.29489437\n",
      "Iteration 14, loss = 0.27855658\n",
      "Iteration 15, loss = 0.26350557\n",
      "Iteration 16, loss = 0.24911145\n",
      "Iteration 17, loss = 0.23527230\n",
      "Iteration 18, loss = 0.22198951\n",
      "Iteration 19, loss = 0.20918275\n",
      "Iteration 20, loss = 0.19679826\n",
      "Iteration 21, loss = 0.18482787\n",
      "Iteration 22, loss = 0.17333673\n",
      "Iteration 23, loss = 0.16244474\n",
      "Iteration 24, loss = 0.15226559\n",
      "Iteration 25, loss = 0.14287097\n",
      "Iteration 26, loss = 0.13429254\n",
      "Iteration 27, loss = 0.12654158\n",
      "Iteration 28, loss = 0.11960936\n",
      "Iteration 29, loss = 0.11344815\n",
      "Iteration 30, loss = 0.10800310\n",
      "Iteration 31, loss = 0.10322739\n",
      "Iteration 32, loss = 0.09902800\n",
      "Iteration 33, loss = 0.09533389\n",
      "Iteration 34, loss = 0.09206149\n",
      "Iteration 35, loss = 0.08915355\n",
      "Iteration 36, loss = 0.08651164\n",
      "Iteration 37, loss = 0.08410219\n",
      "Iteration 38, loss = 0.08189085\n",
      "Iteration 39, loss = 0.07984823\n",
      "Iteration 40, loss = 0.07795653\n",
      "Iteration 41, loss = 0.07620230\n",
      "Iteration 42, loss = 0.07457171\n",
      "Iteration 43, loss = 0.07306074\n",
      "Iteration 44, loss = 0.07166124\n",
      "Iteration 45, loss = 0.07036636\n",
      "Iteration 46, loss = 0.06917159\n",
      "Iteration 47, loss = 0.06807199\n",
      "Iteration 48, loss = 0.06706208\n",
      "Iteration 49, loss = 0.06613767\n",
      "Iteration 50, loss = 0.06529104\n",
      "Iteration 51, loss = 0.06451675\n",
      "Iteration 52, loss = 0.06380757\n",
      "Iteration 53, loss = 0.06315650\n",
      "Iteration 54, loss = 0.06255613\n",
      "Iteration 55, loss = 0.06200054\n",
      "Iteration 56, loss = 0.06148436\n",
      "Iteration 57, loss = 0.06100282\n",
      "Iteration 58, loss = 0.06055187\n",
      "Iteration 59, loss = 0.06012807\n",
      "Iteration 60, loss = 0.05972813\n",
      "Iteration 61, loss = 0.05934928\n",
      "Iteration 62, loss = 0.05898944\n",
      "Iteration 63, loss = 0.05864686\n",
      "Iteration 64, loss = 0.05832015\n",
      "Iteration 65, loss = 0.05800815\n",
      "Iteration 66, loss = 0.05771022\n",
      "Iteration 67, loss = 0.05742552\n",
      "Iteration 68, loss = 0.05715307\n",
      "Iteration 69, loss = 0.05689219\n",
      "Iteration 70, loss = 0.05664224\n",
      "Iteration 71, loss = 0.05640260\n",
      "Iteration 72, loss = 0.05617264\n",
      "Iteration 73, loss = 0.05595186\n",
      "Iteration 74, loss = 0.05573982\n",
      "Iteration 75, loss = 0.05553669\n",
      "Iteration 76, loss = 0.05534131\n",
      "Iteration 77, loss = 0.05515299\n",
      "Iteration 78, loss = 0.05497126\n",
      "Iteration 79, loss = 0.05479565\n",
      "Iteration 80, loss = 0.05462579\n",
      "Iteration 81, loss = 0.05446128\n",
      "Iteration 82, loss = 0.05430176\n",
      "Iteration 83, loss = 0.05414694\n",
      "Iteration 84, loss = 0.05399654\n",
      "Iteration 85, loss = 0.05385031\n",
      "Iteration 86, loss = 0.05370834\n",
      "Iteration 87, loss = 0.05357062\n",
      "Iteration 88, loss = 0.05343657\n",
      "Iteration 89, loss = 0.05330598\n",
      "Iteration 90, loss = 0.05317868\n",
      "Iteration 91, loss = 0.05305452\n",
      "Iteration 92, loss = 0.05293338\n",
      "Iteration 93, loss = 0.05281512\n",
      "Iteration 94, loss = 0.05269964\n",
      "Iteration 95, loss = 0.05258683\n",
      "Iteration 96, loss = 0.05247945\n",
      "Iteration 97, loss = 0.05238325\n",
      "Iteration 98, loss = 0.05228941\n",
      "Iteration 99, loss = 0.05219785\n",
      "Iteration 100, loss = 0.05210848\n",
      "Iteration 101, loss = 0.05202121\n",
      "Iteration 102, loss = 0.05193597\n",
      "Iteration 103, loss = 0.05185268\n",
      "Iteration 104, loss = 0.05177125\n",
      "Iteration 105, loss = 0.05169163\n",
      "Iteration 106, loss = 0.05161374\n",
      "Iteration 107, loss = 0.05153753\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "Accuracy on the test set: 100.00\n",
      "Accuracy: 100.00\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        10\n",
      "           1       1.00      1.00      1.00         9\n",
      "           2       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Create a neural network classifier using scikit-learn's MLPClassifier\n",
    "mlp = MLPClassifier(\n",
    "    hidden_layer_sizes=(10,),\n",
    "    max_iter=1000,\n",
    "    random_state=42,\n",
    "    solver=\"sgd\",\n",
    "    verbose=1,\n",
    "    tol=1e-4,\n",
    "    learning_rate_init=0.1,\n",
    ")\n",
    "\n",
    "# Fit the model to the training data\n",
    "mlp.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "accuracy = mlp.score(X_test, y_test)\n",
    "print(f\"Accuracy on the test set: {accuracy * 100:.2f}\")\n",
    "\n",
    "\n",
    "# Evaluate the performance\n",
    "y_pred = mlp.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy * 100:.2f}\")\n",
    "\n",
    "# Display classification report\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline\n",
    "\n",
    "In scikit-learn, a Pipeline is a way to streamline a lot of the routine processes, especially in the context of `feature preprocessing and model building`. It sequentially applies a list of transforms and a final estimator. Intermediate steps of the pipeline must be transformers (i.e., they must implement the fit and transform methods), while the final estimator only needs to implement the fit method.\n",
    "\n",
    "The main purpose of a Pipeline is to assemble several steps that can be cross-validated together while setting different parameters. This ensures that each step in the process is applied in the correct order.\n",
    "\n",
    "Here's a simple example using a pipeline with StandardScaler and MLPClassifier:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0]\n",
      "[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Create a pipeline with StandardScaler and MLPClassifier\n",
    "pipeline = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000, random_state=42),\n",
    ")\n",
    "\n",
    "# Fit the pipeline on the training data\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Predict using the pipeline\n",
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "print(y_pred)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to use 5-fold cross-validation to find accuracy scores and their average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.36430638\n",
      "Iteration 2, loss = 1.18638437\n",
      "Iteration 3, loss = 1.00430655\n",
      "Iteration 4, loss = 0.84704254\n",
      "Iteration 5, loss = 0.71719051\n",
      "Iteration 6, loss = 0.61047342\n",
      "Iteration 7, loss = 0.52313618\n",
      "Iteration 8, loss = 0.45239689\n",
      "Iteration 9, loss = 0.39527744\n",
      "Iteration 10, loss = 0.35048384\n",
      "Iteration 11, loss = 0.31668698\n",
      "Iteration 12, loss = 0.29166899\n",
      "Iteration 13, loss = 0.27259234\n",
      "Iteration 14, loss = 0.25686986\n",
      "Iteration 15, loss = 0.24287902\n",
      "Iteration 16, loss = 0.23014230\n",
      "Iteration 17, loss = 0.21853451\n",
      "Iteration 18, loss = 0.20789177\n",
      "Iteration 19, loss = 0.19811174\n",
      "Iteration 20, loss = 0.18896397\n",
      "Iteration 21, loss = 0.18024311\n",
      "Iteration 22, loss = 0.17184486\n",
      "Iteration 23, loss = 0.16375180\n",
      "Iteration 24, loss = 0.15599896\n",
      "Iteration 25, loss = 0.14862289\n",
      "Iteration 26, loss = 0.14166167\n",
      "Iteration 27, loss = 0.13511271\n",
      "Iteration 28, loss = 0.12897770\n",
      "Iteration 29, loss = 0.12326143\n",
      "Iteration 30, loss = 0.11797185\n",
      "Iteration 31, loss = 0.11312110\n",
      "Iteration 32, loss = 0.10870332\n",
      "Iteration 33, loss = 0.10469876\n",
      "Iteration 34, loss = 0.10108286\n",
      "Iteration 35, loss = 0.09782786\n",
      "Iteration 36, loss = 0.09490579\n",
      "Iteration 37, loss = 0.09228393\n",
      "Iteration 38, loss = 0.08993143\n",
      "Iteration 39, loss = 0.08781899\n",
      "Iteration 40, loss = 0.08591733\n",
      "Iteration 41, loss = 0.08419727\n",
      "Iteration 42, loss = 0.08263495\n",
      "Iteration 43, loss = 0.08120052\n",
      "Iteration 44, loss = 0.07987413\n",
      "Iteration 45, loss = 0.07863941\n",
      "Iteration 46, loss = 0.07748606\n",
      "Iteration 47, loss = 0.07640092\n",
      "Iteration 48, loss = 0.07537587\n",
      "Iteration 49, loss = 0.07440530\n",
      "Iteration 50, loss = 0.07348518\n",
      "Iteration 51, loss = 0.07261285\n",
      "Iteration 52, loss = 0.07178931\n",
      "Iteration 53, loss = 0.07101067\n",
      "Iteration 54, loss = 0.07027554\n",
      "Iteration 55, loss = 0.06958191\n",
      "Iteration 56, loss = 0.06893039\n",
      "Iteration 57, loss = 0.06831634\n",
      "Iteration 58, loss = 0.06773775\n",
      "Iteration 59, loss = 0.06719259\n",
      "Iteration 60, loss = 0.06667822\n",
      "Iteration 61, loss = 0.06619204\n",
      "Iteration 62, loss = 0.06573166\n",
      "Iteration 63, loss = 0.06529465\n",
      "Iteration 64, loss = 0.06487849\n",
      "Iteration 65, loss = 0.06448098\n",
      "Iteration 66, loss = 0.06410039\n",
      "Iteration 67, loss = 0.06373504\n",
      "Iteration 68, loss = 0.06338346\n",
      "Iteration 69, loss = 0.06304531\n",
      "Iteration 70, loss = 0.06271903\n",
      "Iteration 71, loss = 0.06240344\n",
      "Iteration 72, loss = 0.06209786\n",
      "Iteration 73, loss = 0.06180234\n",
      "Iteration 74, loss = 0.06151588\n",
      "Iteration 75, loss = 0.06123802\n",
      "Iteration 76, loss = 0.06096847\n",
      "Iteration 77, loss = 0.06070699\n",
      "Iteration 78, loss = 0.06045327\n",
      "Iteration 79, loss = 0.06020740\n",
      "Iteration 80, loss = 0.05996891\n",
      "Iteration 81, loss = 0.05973709\n",
      "Iteration 82, loss = 0.05951163\n",
      "Iteration 83, loss = 0.05930208\n",
      "Iteration 84, loss = 0.05910706\n",
      "Iteration 85, loss = 0.05891756\n",
      "Iteration 86, loss = 0.05873335\n",
      "Iteration 87, loss = 0.05855415\n",
      "Iteration 88, loss = 0.05837970\n",
      "Iteration 89, loss = 0.05820979\n",
      "Iteration 90, loss = 0.05804423\n",
      "Iteration 91, loss = 0.05788278\n",
      "Iteration 92, loss = 0.05772525\n",
      "Iteration 93, loss = 0.05757147\n",
      "Iteration 94, loss = 0.05742128\n",
      "Iteration 95, loss = 0.05727454\n",
      "Iteration 96, loss = 0.05713111\n",
      "Iteration 97, loss = 0.05699089\n",
      "Iteration 98, loss = 0.05685376\n",
      "Iteration 99, loss = 0.05671961\n",
      "Iteration 100, loss = 0.05658836\n",
      "Iteration 101, loss = 0.05645991\n",
      "Iteration 102, loss = 0.05633419\n",
      "Iteration 103, loss = 0.05621110\n",
      "Iteration 104, loss = 0.05609058\n",
      "Iteration 105, loss = 0.05597255\n",
      "Iteration 106, loss = 0.05585694\n",
      "Iteration 107, loss = 0.05574381\n",
      "Iteration 108, loss = 0.05563297\n",
      "Iteration 109, loss = 0.05552440\n",
      "Iteration 110, loss = 0.05541803\n",
      "Iteration 111, loss = 0.05531375\n",
      "Iteration 112, loss = 0.05521149\n",
      "Iteration 113, loss = 0.05511122\n",
      "Iteration 114, loss = 0.05501287\n",
      "Iteration 115, loss = 0.05491638\n",
      "Iteration 116, loss = 0.05482170\n",
      "Iteration 117, loss = 0.05472879\n",
      "Iteration 118, loss = 0.05463759\n",
      "Iteration 119, loss = 0.05454806\n",
      "Iteration 120, loss = 0.05446017\n",
      "Iteration 121, loss = 0.05437387\n",
      "Iteration 122, loss = 0.05428912\n",
      "Iteration 123, loss = 0.05420588\n",
      "Iteration 124, loss = 0.05412411\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "Iteration 1, loss = 1.37053717\n",
      "Iteration 2, loss = 1.19091642\n",
      "Iteration 3, loss = 1.00974506\n",
      "Iteration 4, loss = 0.85708767\n",
      "Iteration 5, loss = 0.73081847\n",
      "Iteration 6, loss = 0.62724412\n",
      "Iteration 7, loss = 0.54077526\n",
      "Iteration 8, loss = 0.46976021\n",
      "Iteration 9, loss = 0.41275771\n",
      "Iteration 10, loss = 0.36769027\n",
      "Iteration 11, loss = 0.33380578\n",
      "Iteration 12, loss = 0.30857617\n",
      "Iteration 13, loss = 0.28910824\n",
      "Iteration 14, loss = 0.27292356\n",
      "Iteration 15, loss = 0.25835260\n",
      "Iteration 16, loss = 0.24469510\n",
      "Iteration 17, loss = 0.23192846\n",
      "Iteration 18, loss = 0.21993139\n",
      "Iteration 19, loss = 0.20849521\n",
      "Iteration 20, loss = 0.19744082\n",
      "Iteration 21, loss = 0.18668389\n",
      "Iteration 22, loss = 0.17619373\n",
      "Iteration 23, loss = 0.16605006\n",
      "Iteration 24, loss = 0.15636346\n",
      "Iteration 25, loss = 0.14723744\n",
      "Iteration 26, loss = 0.13875282\n",
      "Iteration 27, loss = 0.13092988\n",
      "Iteration 28, loss = 0.12379402\n",
      "Iteration 29, loss = 0.11736141\n",
      "Iteration 30, loss = 0.11162416\n",
      "Iteration 31, loss = 0.10657672\n",
      "Iteration 32, loss = 0.10215249\n",
      "Iteration 33, loss = 0.09827784\n",
      "Iteration 34, loss = 0.09488001\n",
      "Iteration 35, loss = 0.09190627\n",
      "Iteration 36, loss = 0.08925092\n",
      "Iteration 37, loss = 0.08686448\n",
      "Iteration 38, loss = 0.08469719\n",
      "Iteration 39, loss = 0.08270115\n",
      "Iteration 40, loss = 0.08085176\n",
      "Iteration 41, loss = 0.07912199\n",
      "Iteration 42, loss = 0.07749574\n",
      "Iteration 43, loss = 0.07596664\n",
      "Iteration 44, loss = 0.07453222\n",
      "Iteration 45, loss = 0.07319134\n",
      "Iteration 46, loss = 0.07194291\n",
      "Iteration 47, loss = 0.07078518\n",
      "Iteration 48, loss = 0.06971581\n",
      "Iteration 49, loss = 0.06873408\n",
      "Iteration 50, loss = 0.06783480\n",
      "Iteration 51, loss = 0.06700902\n",
      "Iteration 52, loss = 0.06625062\n",
      "Iteration 53, loss = 0.06555383\n",
      "Iteration 54, loss = 0.06491311\n",
      "Iteration 55, loss = 0.06432085\n",
      "Iteration 56, loss = 0.06377137\n",
      "Iteration 57, loss = 0.06325952\n",
      "Iteration 58, loss = 0.06278073\n",
      "Iteration 59, loss = 0.06233155\n",
      "Iteration 60, loss = 0.06190897\n",
      "Iteration 61, loss = 0.06150938\n",
      "Iteration 62, loss = 0.06113047\n",
      "Iteration 63, loss = 0.06077037\n",
      "Iteration 64, loss = 0.06042755\n",
      "Iteration 65, loss = 0.06010076\n",
      "Iteration 66, loss = 0.05978900\n",
      "Iteration 67, loss = 0.05949147\n",
      "Iteration 68, loss = 0.05920753\n",
      "Iteration 69, loss = 0.05893626\n",
      "Iteration 70, loss = 0.05867697\n",
      "Iteration 71, loss = 0.05842901\n",
      "Iteration 72, loss = 0.05819175\n",
      "Iteration 73, loss = 0.05796457\n",
      "Iteration 74, loss = 0.05774683\n",
      "Iteration 75, loss = 0.05753790\n",
      "Iteration 76, loss = 0.05733717\n",
      "Iteration 77, loss = 0.05714407\n",
      "Iteration 78, loss = 0.05695803\n",
      "Iteration 79, loss = 0.05677856\n",
      "Iteration 80, loss = 0.05660518\n",
      "Iteration 81, loss = 0.05643746\n",
      "Iteration 82, loss = 0.05627518\n",
      "Iteration 83, loss = 0.05611797\n",
      "Iteration 84, loss = 0.05596540\n",
      "Iteration 85, loss = 0.05581721\n",
      "Iteration 86, loss = 0.05567315\n",
      "Iteration 87, loss = 0.05553301\n",
      "Iteration 88, loss = 0.05539662\n",
      "Iteration 89, loss = 0.05526380\n",
      "Iteration 90, loss = 0.05513441\n",
      "Iteration 91, loss = 0.05500829\n",
      "Iteration 92, loss = 0.05488531\n",
      "Iteration 93, loss = 0.05476536\n",
      "Iteration 94, loss = 0.05464834\n",
      "Iteration 95, loss = 0.05453414\n",
      "Iteration 96, loss = 0.05442303\n",
      "Iteration 97, loss = 0.05431468\n",
      "Iteration 98, loss = 0.05420884\n",
      "Iteration 99, loss = 0.05410542\n",
      "Iteration 100, loss = 0.05400432\n",
      "Iteration 101, loss = 0.05390544\n",
      "Iteration 102, loss = 0.05380870\n",
      "Iteration 103, loss = 0.05371401\n",
      "Iteration 104, loss = 0.05362128\n",
      "Iteration 105, loss = 0.05353045\n",
      "Iteration 106, loss = 0.05344153\n",
      "Iteration 107, loss = 0.05335442\n",
      "Iteration 108, loss = 0.05326900\n",
      "Iteration 109, loss = 0.05318521\n",
      "Iteration 110, loss = 0.05310300\n",
      "Iteration 111, loss = 0.05302230\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "Iteration 1, loss = 1.36441069\n",
      "Iteration 2, loss = 1.18928975\n",
      "Iteration 3, loss = 1.01073523\n",
      "Iteration 4, loss = 0.85269461\n",
      "Iteration 5, loss = 0.71890050\n",
      "Iteration 6, loss = 0.61182349\n",
      "Iteration 7, loss = 0.52463950\n",
      "Iteration 8, loss = 0.45271891\n",
      "Iteration 9, loss = 0.39323726\n",
      "Iteration 10, loss = 0.34631670\n",
      "Iteration 11, loss = 0.31111222\n",
      "Iteration 12, loss = 0.28526401\n",
      "Iteration 13, loss = 0.26554056\n",
      "Iteration 14, loss = 0.24936608\n",
      "Iteration 15, loss = 0.23511561\n",
      "Iteration 16, loss = 0.22195200\n",
      "Iteration 17, loss = 0.20955486\n",
      "Iteration 18, loss = 0.19784594\n",
      "Iteration 19, loss = 0.18667766\n",
      "Iteration 20, loss = 0.17588505\n",
      "Iteration 21, loss = 0.16537519\n",
      "Iteration 22, loss = 0.15512473\n",
      "Iteration 23, loss = 0.14519038\n",
      "Iteration 24, loss = 0.13565787\n",
      "Iteration 25, loss = 0.12663978\n",
      "Iteration 26, loss = 0.11822399\n",
      "Iteration 27, loss = 0.11046435\n",
      "Iteration 28, loss = 0.10339463\n",
      "Iteration 29, loss = 0.09701325\n",
      "Iteration 30, loss = 0.09130672\n",
      "Iteration 31, loss = 0.08624821\n",
      "Iteration 32, loss = 0.08177146\n",
      "Iteration 33, loss = 0.07779398\n",
      "Iteration 34, loss = 0.07432552\n",
      "Iteration 35, loss = 0.07128922\n",
      "Iteration 36, loss = 0.06861213\n",
      "Iteration 37, loss = 0.06625795\n",
      "Iteration 38, loss = 0.06414392\n",
      "Iteration 39, loss = 0.06221070\n",
      "Iteration 40, loss = 0.06042450\n",
      "Iteration 41, loss = 0.05875940\n",
      "Iteration 42, loss = 0.05720561\n",
      "Iteration 43, loss = 0.05574942\n",
      "Iteration 44, loss = 0.05438351\n",
      "Iteration 45, loss = 0.05310685\n",
      "Iteration 46, loss = 0.05191619\n",
      "Iteration 47, loss = 0.05080816\n",
      "Iteration 48, loss = 0.04978189\n",
      "Iteration 49, loss = 0.04883494\n",
      "Iteration 50, loss = 0.04796359\n",
      "Iteration 51, loss = 0.04716373\n",
      "Iteration 52, loss = 0.04643060\n",
      "Iteration 53, loss = 0.04575906\n",
      "Iteration 54, loss = 0.04514371\n",
      "Iteration 55, loss = 0.04457916\n",
      "Iteration 56, loss = 0.04406010\n",
      "Iteration 57, loss = 0.04358156\n",
      "Iteration 58, loss = 0.04313916\n",
      "Iteration 59, loss = 0.04272847\n",
      "Iteration 60, loss = 0.04234563\n",
      "Iteration 61, loss = 0.04198801\n",
      "Iteration 62, loss = 0.04165201\n",
      "Iteration 63, loss = 0.04133519\n",
      "Iteration 64, loss = 0.04103570\n",
      "Iteration 65, loss = 0.04075198\n",
      "Iteration 66, loss = 0.04048226\n",
      "Iteration 67, loss = 0.04022540\n",
      "Iteration 68, loss = 0.03998043\n",
      "Iteration 69, loss = 0.03974658\n",
      "Iteration 70, loss = 0.03952314\n",
      "Iteration 71, loss = 0.03930986\n",
      "Iteration 72, loss = 0.03910649\n",
      "Iteration 73, loss = 0.03891197\n",
      "Iteration 74, loss = 0.03872588\n",
      "Iteration 75, loss = 0.03854774\n",
      "Iteration 76, loss = 0.03837714\n",
      "Iteration 77, loss = 0.03821369\n",
      "Iteration 78, loss = 0.03805704\n",
      "Iteration 79, loss = 0.03790676\n",
      "Iteration 80, loss = 0.03776249\n",
      "Iteration 81, loss = 0.03762388\n",
      "Iteration 82, loss = 0.03749058\n",
      "Iteration 83, loss = 0.03736227\n",
      "Iteration 84, loss = 0.03723865\n",
      "Iteration 85, loss = 0.03711946\n",
      "Iteration 86, loss = 0.03700441\n",
      "Iteration 87, loss = 0.03689330\n",
      "Iteration 88, loss = 0.03678595\n",
      "Iteration 89, loss = 0.03668210\n",
      "Iteration 90, loss = 0.03658194\n",
      "Iteration 91, loss = 0.03648476\n",
      "Iteration 92, loss = 0.03639041\n",
      "Iteration 93, loss = 0.03629875\n",
      "Iteration 94, loss = 0.03620966\n",
      "Iteration 95, loss = 0.03612344\n",
      "Iteration 96, loss = 0.03603958\n",
      "Iteration 97, loss = 0.03595803\n",
      "Iteration 98, loss = 0.03587869\n",
      "Iteration 99, loss = 0.03580148\n",
      "Iteration 100, loss = 0.03572631\n",
      "Iteration 101, loss = 0.03565327\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "Iteration 1, loss = 1.36536424\n",
      "Iteration 2, loss = 1.18934857\n",
      "Iteration 3, loss = 1.00971561\n",
      "Iteration 4, loss = 0.85453727\n",
      "Iteration 5, loss = 0.72698235\n",
      "Iteration 6, loss = 0.62185733\n",
      "Iteration 7, loss = 0.53532188\n",
      "Iteration 8, loss = 0.46445608\n",
      "Iteration 9, loss = 0.40615461\n",
      "Iteration 10, loss = 0.36030636\n",
      "Iteration 11, loss = 0.32557038\n",
      "Iteration 12, loss = 0.29933856\n",
      "Iteration 13, loss = 0.27870997\n",
      "Iteration 14, loss = 0.26121532\n",
      "Iteration 15, loss = 0.24507580\n",
      "Iteration 16, loss = 0.22970766\n",
      "Iteration 17, loss = 0.21517271\n",
      "Iteration 18, loss = 0.20138770\n",
      "Iteration 19, loss = 0.18820606\n",
      "Iteration 20, loss = 0.17555273\n",
      "Iteration 21, loss = 0.16338346\n",
      "Iteration 22, loss = 0.15173267\n",
      "Iteration 23, loss = 0.14070763\n",
      "Iteration 24, loss = 0.13043045\n",
      "Iteration 25, loss = 0.12099926\n",
      "Iteration 26, loss = 0.11243575\n",
      "Iteration 27, loss = 0.10473194\n",
      "Iteration 28, loss = 0.09786647\n",
      "Iteration 29, loss = 0.09181844\n",
      "Iteration 30, loss = 0.08650151\n",
      "Iteration 31, loss = 0.08185301\n",
      "Iteration 32, loss = 0.07781153\n",
      "Iteration 33, loss = 0.07431630\n",
      "Iteration 34, loss = 0.07123966\n",
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      "Iteration 155, loss = 0.02096676\n",
      "Iteration 156, loss = 0.02087467\n",
      "Iteration 157, loss = 0.02078440\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "Iteration 1, loss = 1.36797352\n",
      "Iteration 2, loss = 1.18583880\n",
      "Iteration 3, loss = 1.00099545\n",
      "Iteration 4, loss = 0.84157584\n",
      "Iteration 5, loss = 0.71441066\n",
      "Iteration 6, loss = 0.61007590\n",
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      "Iteration 47, loss = 0.07465588\n",
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      "Iteration 49, loss = 0.07253458\n",
      "Iteration 50, loss = 0.07159825\n",
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      "Iteration 70, loss = 0.06110659\n",
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      "Iteration 87, loss = 0.05711535\n",
      "Iteration 88, loss = 0.05693804\n",
      "Iteration 89, loss = 0.05676677\n",
      "Iteration 90, loss = 0.05660086\n",
      "Iteration 91, loss = 0.05643917\n",
      "Iteration 92, loss = 0.05628149\n",
      "Iteration 93, loss = 0.05612761\n",
      "Iteration 94, loss = 0.05597738\n",
      "Iteration 95, loss = 0.05583063\n",
      "Iteration 96, loss = 0.05568720\n",
      "Iteration 97, loss = 0.05554695\n",
      "Iteration 98, loss = 0.05540974\n",
      "Iteration 99, loss = 0.05527544\n",
      "Iteration 100, loss = 0.05515231\n",
      "Iteration 101, loss = 0.05503668\n",
      "Iteration 102, loss = 0.05492394\n",
      "Iteration 103, loss = 0.05481382\n",
      "Iteration 104, loss = 0.05470615\n",
      "Iteration 105, loss = 0.05460086\n",
      "Iteration 106, loss = 0.05449788\n",
      "Iteration 107, loss = 0.05439711\n",
      "Iteration 108, loss = 0.05429846\n",
      "Iteration 109, loss = 0.05420187\n",
      "Iteration 110, loss = 0.05410728\n",
      "Iteration 111, loss = 0.05401463\n",
      "Iteration 112, loss = 0.05392384\n",
      "Iteration 113, loss = 0.05383487\n",
      "Iteration 114, loss = 0.05374766\n",
      "Iteration 115, loss = 0.05366215\n",
      "Iteration 116, loss = 0.05357829\n",
      "Iteration 117, loss = 0.05349603\n",
      "Iteration 118, loss = 0.05341532\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "Cross-validation scores: [1.         1.         0.93333333 0.93333333 0.96666667]\n",
      "Mean accuracy: 0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "\n",
    "# Create an MLPClassifier and a pipeline with StandardScaler\n",
    "pipeline = make_pipeline(StandardScaler(), mlp)\n",
    "\n",
    "# Perform 5-fold cross-validation\n",
    "cv_scores = cross_val_score(pipeline, X, y, cv=5)\n",
    "\n",
    "# Display the cross-validation scores\n",
    "print(\"Cross-validation scores:\", cv_scores)\n",
    "print(f\"Mean accuracy: {cv_scores.mean():.2f}\")"
   ]
  }
 ],
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