Machine Learning · Chapter 11 of 40
Logistic Regression
LOGISTIC REGRESSION is a CLASSIFICATION algorithm despite the name. It outputs a probability between 0 and 1 using the sigmoid function.
Excellent baseline for binary classification.
Example 1 (python)
from sklearn.linear_model import LogisticRegression
m = LogisticRegression().fit(X_train, y_train)
print(m.predict_proba(X_test[:1]))Output
[[0.2, 0.8]]Probabilities for each class.
Example 2 (python)
print(m.predict(X_test[:1]))Output
[1]Predicted class label.
Key points
- Classification, not regression.
- Outputs probabilities via sigmoid.
- Great baseline for binary tasks.
- Fast, interpretable, well-calibrated.
💡 Note: Multi-class works via 'one-vs-rest' by default — scikit-learn handles it automatically.