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- 3.4. Metrics and scoring: quantifying the quality of predictions
- Classification of text documents using sparse features
- Explicit feature map approximation for RBF kernels
- Permutation Importance vs Random Forest Feature Importance (MDI)
- Post pruning decision trees with cost complexity pruning
- sklearn.metrics.accuracy_score (Python function, in accuracy_score)
- sklearn.metrics.balanced_accuracy_score (Python function, in balanced_accuracy_score)
- sklearn.metrics.top_k_accuracy_score (Python function, in top_k_accuracy_score)
- accuracy_score
- 3.4. Metrics and scoring: quantifying the quality of predictions > Accuracy score
- 1.1. Linear Models
- 1.10. Decision Trees
- 1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking
- 1.12. Multiclass and multioutput algorithms
- 1.13. Feature selection