Testing data with cross-validation
In cross-validation, also known as CV, the training data is split into five folds (any number will do, but five is standard). The ML algorithm is fit on one fold at a time and tested on the remaining data. The result is five different training and test sets that are all representative of the same data. The mean of the scores is usually taken as the accuracy of the model.
Note
For cross-validation, 5 folds is only one suggestion. Any natural number may be used, with 3 and 10 also being fairly common.
Cross-validation is a core tool for ML. Mean test scores on different folds are more reliable than one mean test score on the entire set, which we performed in the first exercise. When examining one test score, there is no way of knowing whether it is low or high. Five test scores give a better picture of the true accuracy of the model.
Cross-validation can be implemented in a variety of ways. A standard approach is to use cross_val_score,...
