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...
What is k-fold cross-validation, and how does it work? A k-fold cross-validation is an approach using which one can estimate the performance of a machine learning model. In k-fold cross-validation, the dataset is divided into k parts called k folds. Then, the (k-1)...
What is polynomial regression? In linear regression, we consider a linear relationship between the target and the predictor variables. But sometimes the target variable does not have a linear relationship with the predictor variables or the features. For example,...
We can use the Extra Trees to get the feature importances of various features of a dataset. These feature importances are impurity-based feature importances. Please note that impurity-based feature importances are strongly biased. They favor numerical features over...
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