Standard 6 stops to get here

Gradient Boosting

An ensemble technique that builds models sequentially, each correcting errors of previous ones (XGBoost, LightGBM, CatBoost).

Your route here

6 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Supervised Learning ✓ understood

    Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.

  3. Ensemble Learning ✓ understood

    Combining multiple models to produce better predictions than any individual model (bagging, boosting, stacking).

  4. Classification ✓ understood

    A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.

  5. Regression ✓ understood

    A supervised learning task where the model predicts continuous numerical values rather than discrete categories.

  6. Decision Tree ✓ understood

    A tree-structured model that makes decisions by splitting data based on feature values, interpretable but prone to overfitting.

  7. Gradient Boosting · you are here ✓ understood

Where it sits

Before this

Ensemble Learning Decision Tree
Gradient Boosting

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Foundations Random Forest An ensemble of decision trees trained on random subsets of data and features, reducing overfitting through averaging. Training Gradient Descent An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss. Foundations Feature Importance Measures indicating which features contribute most to model predictions, useful for interpretation and selection. Foundations Regression A supervised learning task where the model predicts continuous numerical values rather than discrete categories.