Some popular ways to evaluate the performance of the ML model are:
Confusion Matrix: It is N*N table with different sets of value that is used to determine the performance of the classification model in machine learning.
F1 score: It is the harmonic mean of precision and recall, which is used as one of the best metrics to evaluate the ML model.
Gain and lift charts: Gain & Lift charts are used to determine the rank ordering of the probabilities.
AUC-ROC curve: The AUC-ROC is another performance metric. The ROC is the plot between the sensitivity.
Gini Coefficient: It is used in the classification problems, also known as the Gini Index. It determines the inequality between the values of variables. The high value of the Gini represents a good model.
Root mean squared error: It is one of the most popular metrics used for the evaluation of the regression model. It works by assuming that errors are unbiased and have a normal distribution.
Cross-Validation: It is another popular technique for evaluating the performance of the machine learning model. In this, the models are trained on subsets of the input data and evaluated on the complementary subset of the data.