Regression

Code

import numpy as np
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

X, y = make_regression(n_samples=500, n_features=10, noise=10, random_state=42)
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42)

models = {
    "linear": LinearRegression(),
    "ridge": Ridge(alpha=1.0),
    "rf": RandomForestRegressor(n_estimators=100, random_state=42),
}

for name, m in models.items():
    m.fit(X_tr, y_tr)
    pred = m.predict(X_te)
    rmse = np.sqrt(mean_squared_error(y_te, pred))
    r2 = r2_score(y_te, pred)
    print(f"{name}: rmse={rmse:.3f} r2={r2:.3f}")

Explanation

Regressors predict continuous targets and share the same fit/predict interface as classifiers. LinearRegression fits least squares, while Ridge adds L2 regularization to reduce overfitting on collinear features. RMSE reports error in target units and R-squared indicates the share of variance explained by the model.

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