Model evaluations and improvement

In this section, we show a few metrics to evaluate different machine learning approaches, and how to improve the model performance

Note

More metrics are to be added

Model evaluation

Over and under fitting under_overfitting.pdf.
Bias and variance (and the relationships to under/over fitting) bias_and_variance.pdf.
RUC and AUC roc_and_auc.pdf.

Feature importance

Introduction feature_importance_introduction.pdf.
Gini-impurity method feature_importance_gini.pdf.
Permutation method feature_importance_permutation.pdf.

Model improvement

Regularization regularization.pdf.