Machine Learning with Scikit-Learn
Build, train, and evaluate ML models for classification, regression, and clustering.
Quick Start
Classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Predict
predictions = model.predict(X_test)
probabilities = model.predict_proba(X_test)
# Evaluate
print(classification_report(y_test, predictions))Regression
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_absolute_error, r2_score
model = GradientBoostingRegressor(n_estimators=100)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(f"MAE: {mean_absolute_error(y_test, predictions):.2f}")
print(f"R²: {r2_score(y_test, predictions):.3f}")Clustering
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# Find optimal k (elbow method)
inertias = []
for k in range(1, 11):
km = KMeans(n_clusters=k, random_state=42)
km.fit(X)
inertias.append(km.inertia_)
plt.plot(range(1, 11), inertias, marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.show()
# Train with optimal k
kmeans = KMeans(n_clusters=5, random_state=42)
clusters = kmeans.fit_predict(X)Model Selection Guide
Classification:
- Logistic Regression: Linear, interpretable, baseline
- Random Forest: Non-linear, feature importance, robust
- XGBoost: Best performance, handles missing data
- SVM: Small datasets, kernel trick
Regression:
- Linear Regression: Linear relationships, interpretable
- Ridge/Lasso: Regularization, feature selection
- Random Forest: Non-linear, robust to outliers
- XGBoost: Best performance, often wins competitions
Clustering:
- K-Means: Fast, spherical clusters
- DBSCAN: Arbitrary shapes, handles noise
- Hierarchical: Dendrogram, no k selection
Evaluation Metrics
Classification:
from sklearn.metrics import (
accuracy_score, precision_score, recall_score,
f1_score, roc_auc_score, confusion_matrix
)
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred, average='weighted')
recall = recall_score(y_true, y_pred, average='weighted')
f1 = f1_score(y_true, y_pred, average='weighted')
roc_auc = roc_auc_score(y_true, y_pred_proba, multi_class='ovr')Regression:
from sklearn.metrics import (
mean_absolute_error, mean_squared_error, r2_score
)
mae = mean_absolute_error(y_true, y_pred)
mse = mean_squared_error(y_true, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_true, y_pred)Cross-Validation
from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5, scoring='f1_weighted')
print(f"CV F1: {scores.mean():.3f} (+/- {scores.std() * 2:.3f})")Hyperparameter Tuning
from sklearn.model_selection import GridSearchCV
param_grid = {
'n_estimators': [100, 200, 300],
'max_depth': [5, 10, 15],
'min_samples_split': [2, 5, 10]
}
grid_search = GridSearchCV(
RandomForestClassifier(),
param_grid,
cv=5,
scoring='f1_weighted',
n_jobs=-1
)
grid_search.fit(X_train, y_train)
print(f"Best params: {grid_search.best_params_}")
print(f"Best score: {grid_search.best_score_:.3f}")
# Use best model
best_model = grid_search.best_estimator_Feature Engineering
from sklearn.preprocessing import StandardScaler, LabelEncoder
# Scaling
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Encoding
encoder = LabelEncoder()
y_encoded = encoder.fit_transform(y)
# Polynomial features
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)Pipeline
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
pipeline = Pipeline([
('scaler', StandardScaler()),
('classifier', RandomForestClassifier(n_estimators=100))
])
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)Best Practices
- Always split data before preprocessing
- Use cross-validation for reliable estimates
- Scale features for distance-based models
- Handle class imbalance (SMOTE, class weights)
- Check for overfitting (train vs test performance)
- Save models with joblib or pickle