Logistic Regression Classifier on Iris Dataset

Introduction

In this lab, we will use Logistic Regression Classifier to classify the first two features of Iris dataset based on their labels. We will use scikit-learn library to load and preprocess the dataset, create an instance of Logistic Regression Classifier, and fit the data. Finally, we will display the decision boundaries on the scatter plot.

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Skills Graph

Load the Dataset and Preprocess

We will use scikit-learn library to load the Iris dataset. The dataset contains 3 classes of 50 instances each, where each class refers to a type of iris plant. Each instance has 4 features: sepal length, sepal width, petal length, and petal width.

import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay

## load the Iris dataset
iris = datasets.load_iris()
X = iris.data[:, :2]  ## we only take the first two features.
Y = iris.target

Create an Instance of Logistic Regression Classifier and Fit the Data

We will create an instance of Logistic Regression Classifier and fit the data.

## Create an instance of Logistic Regression Classifier and fit the data.
logreg = LogisticRegression(C=1e5)
logreg.fit(X, Y)

Display the Decision Boundaries on the Scatter Plot

We will display the decision boundaries on the scatter plot using DecisionBoundaryDisplay from scikit-learn library.

_, ax = plt.subplots(figsize=(4, 3))
DecisionBoundaryDisplay.from_estimator(
    logreg,
    X,
    cmap=plt.cm.Paired,
    ax=ax,
    response_method="predict",
    plot_method="pcolormesh",
    shading="auto",
    xlabel="Sepal length",
    ylabel="Sepal width",
    eps=0.5,
)

## Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)

plt.xticks(())
plt.yticks(())

plt.show()

Summary

In this lab, we used Logistic Regression Classifier to classify the first two features of Iris dataset based on their labels. We loaded and preprocessed the dataset using scikit-learn library, created an instance of Logistic Regression Classifier, and fit the data. Finally, we displayed the decision boundaries on the scatter plot.