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...
What is Leave One Out Cross Validation, and how does it work? In our previous article, we discussed what k-fold cross-validation is and how to implement it using sklearn in Python. Leave One Out Cross Validation is a specific variation of k-fold cross-validation where...
In one of our previous articles, we discussed polynomial regression using the sklearn Python library. We read the “mpg” dataset from the seaborn library and created a polynomial regression model that takes the horsepower of a car as input and determines its mpg or...
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