📈

Linear Regression

Regression

🎯

Logistic Regression

Classification

Which should you use?

Linear regression predicts continuous values, while logistic regression classifies into categories. Choose based on your target variable type.

Key Differences

Factor

Linear Regression

Logistic Regression

Output

Continuous value

Probability (0-1)

Loss Function

Mean Squared Error

Cross-Entropy

Use Case

Regression

Classification

Interpretation

Unit change in X → β change in Y

Unit change in X → β change in log-odds

Use Linear Regression when:

Predicting continuous values (prices, temperatures, quantities)

Target variable can be any real number

Relationship is approximately linear

Need interpretable coefficients

Learn Linear Regression

Use Logistic Regression when:

Classifying into categories (yes/no, spam/not spam)

Target variable is binary or categorical

Need probability outputs

Decision boundary is approximately linear

Learn Logistic Regression

Real-World Applications

Linear Regression

House prices

Stock forecasting

Sales prediction

Logistic Regression

Spam detection

Disease diagnosis

Customer churn

Ready to learn both algorithms?

Interactive lessons with visualizations and hands-on practice

Explore ML Algorithms

📈

Linear Regression

Regression

🎯

Logistic Regression

Classification

Which should you use?

Linear regression predicts continuous values, while logistic regression classifies into categories. Choose based on your target variable type.

Key Differences

Factor

Linear Regression

Logistic Regression

Output

Continuous value

Probability (0-1)

Loss Function

Mean Squared Error

Cross-Entropy

Use Case

Regression

Classification

Interpretation

Unit change in X → β change in Y

Unit change in X → β change in log-odds

Use Linear Regression when:

Predicting continuous values (prices, temperatures, quantities)

Target variable can be any real number

Relationship is approximately linear

Need interpretable coefficients

Learn Linear Regression

Use Logistic Regression when:

Classifying into categories (yes/no, spam/not spam)

Target variable is binary or categorical

Need probability outputs

Decision boundary is approximately linear

Learn Logistic Regression

Real-World Applications

Linear Regression

House prices

Stock forecasting

Sales prediction

Logistic Regression

Spam detection

Disease diagnosis

Customer churn

Ready to learn both algorithms?

Interactive lessons with visualizations and hands-on practice

Explore ML Algorithms