Machine learning methods and validation

Your learning path

1. 01

   Decision trees, splits and ensembles

   A decision tree partitions feature space using a sequence of tests. Leaf predictions summarize the training examples reaching them.

   University · Notes · 8 questions
2. 02

   Clustering, distances and interpretation

   Clustering groups examples according to a representation and similarity rule. The groups are not automatically true natural categories.

   University · Notes · 8 questions
3. 03

   PCA, variance and dimensionality reduction

   Principal component analysis finds orthogonal directions of high variance in centred data under its standard formulation.

   University · Notes · 8 questions
4. 04

   Cross-validation, grouped data and time-aware splits

   A k-fold procedure rotates held-out folds to estimate performance for a specified training process. Splits and all fitted preprocessing must be controlled.

   University · Notes · 8 questions