GridSearch: the ultimate Machine Learning Tool

The goal of supervised Machine Learning is to build a prediction function based on historical data. This data has independent (explanatory) variables and a target variable (the variable that you want to predict). Once a predictive model has been built, we measure its error on a separate testing data set. We do this using KPIs that allow quantifying the error of the model, for example, the Mean Square Error in a regression context (quantitative target variable) or the Accuracy in a classification context (categorical target variable). The model with the smallest error is generally selected as the best model.