Python code to compute machine learning classification evaluation metrics (Accuracy, AUC-ROC, MCC) using sklearn library

There are several evaluation metrics (e.g., accuracy, AUC-ROC, Mathew correlation coefficient, precision, recall, F1 score, confusion matrix, etc.) that are used to determine the performance of supervised machine learning classification algorithms. The selection of a metric to assess the performance of a classification algorithm depends …

Finding importance of features with forests of trees

In a classification problem, not all features have the same importance to predict the label of a record. Different approaches are used by classification algorithms to determine the important features for the classification. E.g. XGBoost uses one of these three parameters for measuring feature importance: …