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שנה ב/סמסטר ב/למידת מכונה/תרגולים/למידת מכונה תרגול רגרסיה לינארית.pdf

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שנה ב/סמסטר ב/למידת מכונה/תרגולים/תרגול 2/knn.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Concept: K-Nearest Neighbors (KNN) is a simple, instance-based learning algorithm used for both classification and regression tasks. The main idea is to predict the value or class of a new sample based on the \\( k \\) closest samples (neighbors) in the training dataset.\n",
+        "\n",
+        "For classification, the predicted class is the most common class among the \\( k \\) nearest neighbors. For regression, the predicted value is the average (or weighted average) of the values of the \\( k \\) nearest neighbors.\n",
+        "\n",
+        "Key points:\n",
+        "- Distance Metric: Common distance metrics include Euclidean distance, Manhattan distance, and Minkowski distance.\n",
+        "- Choosing \\( k \\): The value of \\( k \\) is a crucial hyperparameter that needs to be chosen carefully. Smaller \\( k \\) values can lead to noise sensitivity, while larger \\( k \\) values can smooth out the decision boundary.\n",
+        "\n",
+        "## Implementation Example\n",
+        "Suppose we have a dataset that records features like sepal length and sepal width to classify the species of iris flowers.\n"
+      ],
+      "metadata": {
+        "id": "aTl4ko9aeEFC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Explanation of the Code\n",
+        "\n",
+        "1. Libraries\n",
+        "2. Data Preparation\n",
+        "3. Train-Test Split\n",
+        "4. Model Training\n",
+        "5. Predictions\n",
+        "6. Evaluation.\n",
+        "7. Visualization: We plot the decision boundary to visualize how the KNN classifier separates the classes.\n",
+        "\n",
+        "#### Evaluation Metrics\n",
+        "\n",
+        "- Confusion Matrix: Shows the counts of true positives, true negatives, false positives, and false negatives.\n",
+        "- Classification Report: Provides precision, recall, F1-score, and support for each class.\n",
+        "\n",
+        "#### Decision Boundary\n",
+        "\n",
+        "The decision boundary plot helps to visualize how the KNN classifier separates the different classes in the feature space. KNN decision boundaries can be quite complex, reflecting the non-linear separability of the data.\n",
+        "\n",
+        "KNN is intuitive and simple but can be computationally expensive, especially with large datasets, since it requires storing and searching through all training instances during prediction. The choice of \\( k \\) and the distance metric are critical to the model's performance.\n"
+      ],
+      "metadata": {
+        "id": "NHi8X2G1eNyX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "aUvEcE07eDMS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 766
+        },
+        "id": "LZaV_ulhd4E7",
+        "outputId": "0f7c40f0-538d-4da0-fc0a-61c666c4b999"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy: 0.7333333333333333\n",
+            "Confusion Matrix:\n",
+            "[[11  0  0]\n",
+            " [ 0  7  6]\n",
+            " [ 0  2  4]]\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       1.00      1.00      1.00        11\n",
+            "           1       0.78      0.54      0.64        13\n",
+            "           2       0.40      0.67      0.50         6\n",
+            "\n",
+            "    accuracy                           0.73        30\n",
+            "   macro avg       0.73      0.74      0.71        30\n",
+            "weighted avg       0.78      0.73      0.74        30\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Import necessary libraries\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "# Example data (Iris dataset)\n",
+        "from sklearn.datasets import load_iris\n",
+        "iris = load_iris()\n",
+        "X = iris.data[:, :2]  # Using sepal length and sepal width as features\n",
+        "y = iris.target\n",
+        "\n",
+        "# Splitting the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+        "\n",
+        "# Creating and training the KNN model with k=5\n",
+        "model = KNeighborsClassifier(n_neighbors=3)\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "# Making predictions\n",
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "# Evaluating the model\n",
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+        "class_report = classification_report(y_test, y_pred)\n",
+        "\n",
+        "print(f\"Accuracy: {accuracy}\")\n",
+        "print(f\"Confusion Matrix:\\n{conf_matrix}\")\n",
+        "print(f\"Classification Report:\\n{class_report}\")\n",
+        "\n",
+        "# Plotting the decision boundary\n",
+        "def plot_decision_boundary(X, y, model):\n",
+        "    h = .02  # step size in the mesh\n",
+        "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+        "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+        "\n",
+        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+        "    Z = Z.reshape(xx.shape)\n",
+        "    plt.contourf(xx, yy, Z, alpha=0.8)\n",
+        "\n",
+        "    sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, palette='bright', edgecolor='k', s=50)\n",
+        "    plt.xlabel('Sepal Length')\n",
+        "    plt.ylabel('Sepal Width')\n",
+        "    plt.title('KNN Decision Boundary')\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_decision_boundary(X_test, y_test, model)"
+      ]
+    }
+  ]
+}

שנה ב/סמסטר ב/למידת מכונה/תרגולים/תרגול 2/sliced_array.npy

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