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Data Science

Regression Algorithms in Machine Learning

Data Science 270 views Nov 14, 2025 1 min read

Introduction

Regression is used to predict continuous values like price, salary, or temperature.

1. Linear Regression


from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test)

2. Polynomial Regression


from sklearn.preprocessing import PolynomialFeatures

poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)

3. Ridge Regression


from sklearn.linear_model import Ridge
model = Ridge(alpha=1.0)

4. Lasso Regression


from sklearn.linear_model import Lasso
model = Lasso(alpha=0.1)

5. Random Forest Regression


from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()

6. Evaluation Metrics

  • MAE – Mean Absolute Error
  • MSE – Mean Squared Error
  • RMSE – Root Mean Squared Error
  • R² Score

Conclusion

Regression algorithms help solve prediction tasks involving continuous values. Choosing the right model depends on dataset size, complexity, and feature relationships.

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