We can evaluate the performance of an algorithm of a classification problem using the logistic loss function. The logistic loss of a classification algorithm is given by the following formula: $latex Log\ Loss=\frac{-1}{N}\sum_{i=1}^{N}[y_i\log p_i+(1-y_i)\log...
In the repeated random train-test split or shuffle split, the dataset is split into a certain number of folds. Each fold is divided into train and test sets. The machine learning model then uses the train test to learn from the dataset and uses the test set to...
What is the Lasso regression? In our previous article, we discussed the Ridge regression. Using the Ridge regression, we penalize less important predictor variables so that the variables are kept, but their effects are less. In linear regression, we calculate the...
In one of our previous articles, we discussed k-fold cross-validation. Stratified k-fold cross-validation is a variation of k-fold cross-validation, in which stratified folds are returned. In other words, each set contains approximately the same ratio of the target...
What is Ridge regression? If there are too many predictor variables or features, then that may cause various problems. So, we can find important variables and keep them and omit the unimportant ones. But, in a practical scenario, it may not be the case where a...
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