52. What are the ways for examining the performance of the ML model?

1. Confusion Matrix

This is an N*N table with different sets of values. This is used for determining the performance of the classification model in machine learning.

2. F1 score

This is the harmonic mean of precision and recall used as one of the best metrics to evaluate the ML model.

3. Gain and lift charts: 

They are used for determining the rank ordering of the probabilities.

4. AUC-ROC curve

This is a performance metric in which the ROC is the plot between the sensitivity.

5. Gini Coefficient

This is used in classification problems for determining the inequality between the values of variables.

6. Root mean squared error

This is used for evaluating the regression model.