The MSE metric measures the average of the squares of the errors, that is the average squared difference between the estimated values and the actual value. The MSE is a measure of the quality of an estimator—it is always non-negative, and values...

Log loss, also called logistic regression loss or cross-entropy loss, is defined on probability estimates.The metric looks at how well a model can classify a binary target, log loss evaluates how close a model’s predicted values (uncalibrated probability...

The Gini coefficient is a well-established method to quantify the inequality among values of a frequency distribution, and can be used to measure the quality of a binary classifier. Gini is measured between 0 and 1. A Gini index of 0 expresses perfect...

A receiver operating characteristic (ROC), or simply ROC curve, is a graphical plot which illustrates the performance of a binary classifier system as its discrimination threshold is varied. It is created by plotting the fraction of true positives...

It’s the process of balancing a data set by discarding examples of the overrepresented class so that each has the same amount of examples.
A balanced data set allows a model to learn equal amounts of characteristics from each one of the classes...

Hyperaparameter is a parameter whose value is used to control the learning process of a specific AI model. A hyperparameter has to be set / fixed before starting the training process.