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Multi-Class Logistic Regression in SciKit Learn
Logistic Regression is a popular machine learning algorithm used for binary classification tasks. However, it can also be extended to handle multi-class classification problems through techniques like one-vs-rest (OvR) or one-vs-one (OvO) strategies. In this article, we will focus on using the logistic regression model in the SciKit Learn library for multi-class classification.
SciKit Learn is a powerful library in Python for machine learning tasks. It provides a simple interface for implementing various machine learning algorithms, including logistic regression. To use logistic regression for multi-class classification in SciKit Learn, we can simply specify the ‘multi_class’ parameter as ‘multinomial’ or ‘ovr’ (which stands for one-vs-rest).
Example Code
Let’s take a look at an example code snippet to demonstrate how to use multi-class logistic regression in SciKit Learn:
“`
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load the iris dataset
data = load_iris()
X = data.data
y = data.target
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Create a logistic regression model
model = LogisticRegression(multi_class=’multinomial’, max_iter=1000)
# Fit the model to the training data
model.fit(X_train, y_train)
# Make predictions on the test data
y_pred = model.predict(X_test)
# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, y_pred)
print(f”Accuracy: {accuracy}”)
“`
In this code snippet, we first load the iris dataset and split it into training and testing sets. We then create a logistic regression model with the ‘multinomial’ strategy for multi-class classification. After fitting the model to the training data, we make predictions on the test data and calculate the accuracy of the model.
Using logistic regression for multi-class classification in SciKit Learn is straightforward and can be a powerful tool for solving a wide range of classification problems. By leveraging the capabilities of SciKit Learn, we can easily implement and evaluate machine learning models for multi-class classification tasks.
