Gradient Boosting is a powerful machine learning technique that builds strong models by combining weak learners. It minimizes errors using gradient descent and is widely used for accurate predictions in classification and regression tasks.

Tuning machine learning models for peak performance requires more than just good data — it demands smart hyperparameter selection. This post dives into the difference between parameters and hyperparameters, and compares two powerful tuning methods: GridSearchCV and RandomizedSearchCV. Learn how they work, when to use each, and how they can improve your model’s accuracy efficiently.

Lasso Regression adds L1 regularization to linear models, shrinking some coefficients to zero and enabling feature selection. Learn how it handles overfitting and multicollinearity through controlled penalty terms and precise coefficient tuning.