Machine Learning Series – Part 5: Multiclass Logistic Regression Explained Simply

Welcome back to the Machine Learning Series! 🚀 So far, you’ve learned about binary classification using Logistic Regression (Yes/No, True/False, 0/1). But what happens when there are more than two categories to predict?

That’s where Multiclass Logistic Regression comes into play.

What is Multiclass Logistic Regression?

Multiclass Logistic Regression is an extension of logistic regression used when your target variable has three or more classes.

Example:

• Predicting a student’s grade: A, B, C, D, F

• Classifying a fruit: Apple, Banana, Orange

• Detecting an animal type: Cat, Dog, Elephant

How Does It Work?

It uses strategies like:

• One-vs-Rest (OvR): Builds one binary classifier per class (Is it A? Is it B? Is it C?...).

• Multinomial: A true multiclass approach using softmax to compute the probability for each class.

Scikit-learn uses OvR by default, but you can also specify multinomial if using a solver like .

Real-World Use Case

Let’s say you're building a model to predict which department a college student will choose based on their entrance exam scores:

• Options: CSE, ECE, Mechanical, Civil, IT

Python Example with scikit-learn

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.metrics import classification_report

# Sample data (features = exam scores, target = department label)

X = [[80, 85], [60, 75], [90, 88], [40, 55], [70, 70], [65, 50]]

y = [0, 1, 0, 2, 1, 2] # 0: CSE, 1: ECE, 2: Mechanical

# Split data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Create and train model

model = LogisticRegression(multi_class='multinomial', solver='lbfgs')

model.fit(X_train, y_train)

# Predict and evaluate

y_pred = model.predict(X_test)

print(classification_report(y_test, y_pred))

Why Use Multiclass Logistic Regression?

✅ Handles more than two output classes ✅ Still interpretable and efficient ✅ Fast training and good for baseline models ✅ Supported directly in scikit-learn

Limitations

⚠️ Struggles with overlapping classes ⚠️ Not ideal for complex relationships (try Decision Trees or Neural Networks for that)

Summary

• Multiclass Logistic Regression extends binary logistic regression to predict more than two categories.

• It uses strategies like One-vs-Rest and Multinomial to handle multiple classes.

• It’s a powerful starting point for many classification tasks.

#MachineLearningSeries #MulticlassLogisticRegression #Classification #MLForBeginners #DataScience #SupervisedLearning #Python #ScikitLearn #MulticlassClassification #AI