Scikit-learn Introduction

Scikit-learn Overview

Scikit-learn provides a consistent interface for machine learning. It implements common algorithms with unified fit/predict workflow.

Estimators

All models follow the estimator interface. fit(X, y) trains the model. predict(X) makes predictions. predict_proba(X) gives probabilities.

Models have parameters controlling behavior. Parameters are set at initialization. Default values work in many cases.

Data Preprocessing

StandardScaler standardizes features: scaler = StandardScaler(); scaler.fit_transform(X). OneHotEncoder handles categories: encoder = OneHotEncoder().

Imputation: SimpleImputer fills missing values. Pipeline chains transformations: Pipeline([('scaler', StandardScaler()), ('model', LogisticRegression())]).

Model Selection

Train-test split: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2).

Cross-validation: cross_val_score(model, X, y, cv=5). GridSearchCV searches hyperparameters.

Key Takeaways

1. Scikit-learn provides unified interface for ML algorithms
2. Consistent fit/predict workflow simplifies model development
3. Built-in tools handle preprocessing and model selection