scikit-learn

Scikit-learn

Scikit-learn is the foundational Python library for classical machine learning, providing robust implementations of regression, classification, clustering, dimensionality reduction, and pipelines.

When to Use

• Classical Machine Learning Algorithms: Linear models, decision trees, random forests, clustering, and dimensionality reduction.
• End-to-End ML Pipelines: Constructing robust pipelines combining imputers, encoders, scalers, and estimators.
• Cross-Validation & Hyperparameter Tuning: Stratified K-fold validation, GridSearchCV, and RandomizedSearchCV.
• Model Evaluation & Diagnostic Metrics: Confusion matrices, ROC-AUC curves, precision-recall tradeoffs, and classification reports.

Quick Start

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Type-safe, leakage-free ML pipeline
pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', LogisticRegression())
])

pipe.fit(X_train, y_train)
score = pipe.score(X_test, y_test)
print(f"Test Accuracy: {score:.4f}")

Core Concepts

Robust Pipeline with ColumnTransformer

Handling heterogeneous numeric and categorical columns without data leakage:

import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection import train_test_split

# Sample dataset
df = pd.DataFrame({
    'age': [25, 45, np.nan, 35, 52, 23],
    'income': [50000, 120000, 85000, 75000, np.nan, 42000],
    'department': ['sales', 'tech', 'marketing', 'tech', 'sales', 'tech'],
    'promoted': [0, 1, 1, 0, 1, 0]
})

X = df.drop(columns=['promoted'])
y = df['promoted']

numeric_features = ['age', 'income']
categorical_features = ['department']

numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('encoder', OneHotEncoder(handle_unknown='ignore', sparse_output=False))
])

preprocessor = ColumnTransformer(transformers=[
    ('num', numeric_transformer, numeric_features),
    ('cat', categorical_transformer, categorical_features)
])

# Full model pipeline
model_pipeline = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', HistGradientBoostingClassifier(random_state=42))
])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
model_pipeline.fit(X_train, y_train)
print("Test Score:", model_pipeline.score(X_test, y_test))