{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"from matplotlib import pyplot\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.model_selection import train_test_split "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PART 1: Ensemble Learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load Iris Dataset"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sepal length (cm) | \n",
" sepal width (cm) | \n",
" petal length (cm) | \n",
" petal width (cm) | \n",
" target | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 5.1 | \n",
" 3.5 | \n",
" 1.4 | \n",
" 0.2 | \n",
" 0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 4.9 | \n",
" 3.0 | \n",
" 1.4 | \n",
" 0.2 | \n",
" 0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 4.7 | \n",
" 3.2 | \n",
" 1.3 | \n",
" 0.2 | \n",
" 0 | \n",
"
\n",
" \n",
" | 3 | \n",
" 4.6 | \n",
" 3.1 | \n",
" 1.5 | \n",
" 0.2 | \n",
" 0 | \n",
"
\n",
" \n",
" | 4 | \n",
" 5.0 | \n",
" 3.6 | \n",
" 1.4 | \n",
" 0.2 | \n",
" 0 | \n",
"
\n",
" \n",
" | 5 | \n",
" 5.4 | \n",
" 3.9 | \n",
" 1.7 | \n",
" 0.4 | \n",
" 0 | \n",
"
\n",
" \n",
" | 6 | \n",
" 4.6 | \n",
" 3.4 | \n",
" 1.4 | \n",
" 0.3 | \n",
" 0 | \n",
"
\n",
" \n",
" | 7 | \n",
" 5.0 | \n",
" 3.4 | \n",
" 1.5 | \n",
" 0.2 | \n",
" 0 | \n",
"
\n",
" \n",
" | 8 | \n",
" 4.4 | \n",
" 2.9 | \n",
" 1.4 | \n",
" 0.2 | \n",
" 0 | \n",
"
\n",
" \n",
" | 9 | \n",
" 4.9 | \n",
" 3.1 | \n",
" 1.5 | \n",
" 0.1 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"Answer: The performance of the voting classifier (score = 0.95) is the same as that of SVM and Logistic Regression.
\n",
"On the other hand, the performance of the voting classifier (score = 0.95) us much better than that of K-Nearest Neighbour (score = 0.9) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PART 2: Perceptron"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Perceptron Score: 0.9\n"
]
}
],
"source": [
"from sklearn.linear_model import Perceptron\n",
"perceptron_classifier = Perceptron()\n",
"\n",
"# Train Model \n",
"perceptron_classifier.fit(X_train, y_train)\n",
"\n",
"# Evaluate Model\n",
"score = perceptron_classifier.score(X_test, y_test)\n",
"print(f'Perceptron Score: {score}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Question. Compare the performance of perceptron classifier with the ensemble class performance.
\n",
"Answer: The performance of the perceptron classifier (score = 0.9) is not good as that of voting classifier (score = 0.95)"
]
}
],
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