Multi-Class Logistic Regression is a classification algorithm that can be used to predict the target variable in a multi-class classification problem. In this tutorial, we will learn how to implement Multi-Class Logistic Regression using SciKit Learn in Python.
First, make sure you have SciKit Learn installed in your Python environment. You can install it using pip:
pip install scikit-learnNow, let’s begin by importing the necessary libraries:
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<title>Multi-Class Logistic Regression Tutorial</title>
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<h1>Multi-Class Logistic Regression in SciKit Learn</h1>
<p>In this tutorial, we will use SciKit Learn to implement Multi-Class Logistic Regression for a classification problem.</p>
<h2>Step 1: Import Libraries</h2>
<pre>
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
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<h2>Step 2: Load the Dataset</h2>
<p>We will use the Iris dataset, which is a popular dataset for classification problems. It consists of 150 samples with 4 features each: sepal length, sepal width, petal length, and petal width. The target variable is the species of Iris (setosa, versicolor, or virginica).</p>
<pre>
iris = load_iris()
X = iris.data
y = iris.target
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<h2>Step 3: Split the Data</h2>
<p>We will split the dataset into training and testing sets to evaluate the performance of our model.</p>
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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<h2>Step 4: Fit the Model</h2>
<p>Now, we will create an instance of the Logistic Regression model and fit it to the training data.</p>
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clf = LogisticRegression(max_iter=1000)
clf.fit(X_train, y_train)
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<h2>Step 5: Evaluate the Model</h2>
<p>We will use the testing set to evaluate the performance of our model.</p>
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accuracy = clf.score(X_test, y_test)
print('Accuracy:', accuracy)
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<h2>Step 6: Make Predictions</h2>
<p>Finally, we can make predictions on new data using our trained model.</p>
<pre>
new_data = np.array([[5.1, 3.5, 1.4, 0.2]])
prediction = clf.predict(new_data)
print('Prediction:', iris.target_names[prediction][0])
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</html>That’s it! You have successfully implemented Multi-Class Logistic Regression using SciKit Learn. You can further tune the model by exploring different hyperparameters and techniques. Feel free to experiment with different datasets and share your results!
