Machine Learning Complete the lab as given in the notebook file. Include all screenshots of code and output. { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "An7pIRrhKhTb" }, "outputs": [], "source": [ "# Source: https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "source": [ "# Import datasets, classifiers and performance metrics\n", "from sklearn import datasets, svm, metrics\n", "from sklearn.model_selection import train_test_split" ], "metadata": { "id": "m-4vkyOSLHY-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "digits = datasets.load_digits()" ], "metadata": { "id": "JUK7wpLQLJ0Y" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n", "for ax, image, label in zip(axes, digits.images, digits.target):\n", " ax.set_axis_off()\n", " ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n", " ax.set_title(\"Training: %i\" % label)" ], "metadata": { "id": "C2_v9MqbLhQX" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# flatten the images\n", "n_samples = len(digits.images)\n", "data = digits.images.reshape((n_samples, -1))" ], "metadata": { "id": "L65XSQXhLLw0" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Create a classifier: a support vector classifier\n", "clf = svm.SVC(gamma=0.001)" ], "metadata": { "id": "6SO7xR-QMUa9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Split data into 50% train and 50% test subsets\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " data, digits.target, test_size=0.5, shuffle=False\n", ")" ], "metadata": { "id": "B-AEWWiTLlqG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Learn the digits on the train subset\n", "clf.fit(X_train, y_train)" ], "metadata": { "id": "0QXu0qt5LkA_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Predict the value of the digit on the test subset\n", "predicted = clf.predict(X_test)" ], "metadata": { "id": "v-hhRIHWLwB3" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n", "for ax, image, prediction in zip(axes, X_test, predicted):\n", " ax.set_axis_off()\n", " image = image.reshape(8, 8)\n", " ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n", " ax.set_title(f\"Prediction: {prediction}\")" ], "metada.

1. Machine Learning
Complete the lab as given in the notebook file. Include all screenshots of code and output.
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "An7pIRrhKhTb"
},
"outputs": [],
"source": [
"# Source: https://scikit-
learn.org/stable/auto_examples/classification/plot_digits_classification.htmln",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"source": [
"# Import datasets, classifiers and performance metricsn",
"from sklearn import datasets, svm, metricsn",
"from sklearn.model_selection import train_test_split"
],
"metadata": {
"id": "m-4vkyOSLHY-"
},
"execution_count": null,
"outputs": []

2. },
{
"cell_type": "code",
"source": [
"digits = datasets.load_digits()"
],
"metadata": {
"id": "JUK7wpLQLJ0Y"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))n",
"for ax, image, label in zip(axes, digits.images, digits.target):n",
" ax.set_axis_off()n",
" ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest")n",
" ax.set_title("Training: %i" % label)"
],
"metadata": {
"id": "C2_v9MqbLhQX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# flatten the imagesn",
"n_samples = len(digits.images)n",
"data = digits.images.reshape((n_samples, -1))"
],
"metadata": {
"id": "L65XSQXhLLw0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Create a classifier: a support vector classifiern",
"clf = svm.SVC(gamma=0.001)"
],

3. "metadata": {
"id": "6SO7xR-QMUa9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Split data into 50% train and 50% test subsetsn",
"X_train, X_test, y_train, y_test = train_test_split(n",
" data, digits.target, test_size=0.5, shuffle=Falsen",
")"
],
"metadata": {
"id": "B-AEWWiTLlqG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Learn the digits on the train subsetn",
"clf.fit(X_train, y_train)"
],
"metadata": {
"id": "0QXu0qt5LkA_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Predict the value of the digit on the test subsetn",
"predicted = clf.predict(X_test)"
],
"metadata": {
"id": "v-hhRIHWLwB3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",

4. "source": [
"_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))n",
"for ax, image, prediction in zip(axes, X_test, predicted):n",
" ax.set_axis_off()n",
" image = image.reshape(8, 8)n",
" ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest")n",
" ax.set_title(f"Prediction: {prediction}")"
],
"metadata": {
"id": "6sHCRYZOLOvz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(n",
" f"Classification report for classifier {clf}:n"n",
" f"{metrics.classification_report(y_test, predicted)}n"n",
")"
],
"metadata": {
"id": "cn875FFXLQql"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)n",
"disp.figure_.suptitle("Confusion Matrix")n",
"print(f"Confusion matrix:n{disp.confusion_matrix}")n",
"n",
"plt.show()"
],
"metadata": {
"id": "wVU73pCgLSun"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],

5. "metadata": {
"id": "3PpbWPoFLUyb"
},
"execution_count": null,
"outputs": []
}
]
}