The following are some common methods for evaluating the performance of a machine learning model:
Confusion Matrix:
It is a N*N table containing several sets of values that are used to determine the classification model’s performance in machine learning.
F1 score:
One of the most effective measures for evaluating the ML model is the harmonic mean of accuracy and recall.
Gain and lift charts:
Gain and Lift charts are used to establish how the probability should be ranked.
AUC-ROC curve:
Another performance statistic is the AUC-ROC. The plot between the sensitivity and the ROC is known as the ROC.
Gini Coefficient:
It is also known as the Gini Index and is utilized in classification issues. It establishes the disparity in the values of variables. The Gini coefficient’s high value indicates a good model.
Root mean squared error:
It is one of the most often used measures for evaluating regression models. It works by assuming that mistakes have a normal distribution and are impartial.
Cross-Validation:
It’s yet another popular method for assessing the machine learning model’s performance. Models are trained on subsets of the input data and assessed on a complimentary subset of the data in this method.