What are the assumptions of ridge regression?
The assumptions of ridge regression are the same as that of linear regression: linearity, constant variance, and independence. However, as ridge regression does not provide confidence limits, the distribution of errors to be normal need not be assumed.
What is cross validation explained prediction by using ridge regression?
In k-fold cross validation, the training set is split into k smaller sets (or folds). By default, the ridge regression cross validation class uses the Leave One Out strategy (k-fold). We can compare the performance of our model with different alpha values by taking a look at the mean square error.
How is ridge regression an extension of linear regression?
Ridge Regression is an extension of linear regression that adds a regularization penalty to the loss function during training. How to evaluate a Ridge Regression model and use a final model to make predictions for new data. How to configure the Ridge Regression model for a new dataset via grid search and automatically.
How to evaluate ridge regression on a dataset?
We can evaluate the Ridge Regression model on the housing dataset using repeated 10-fold cross-validation and report the average mean absolute error (MAE) on the dataset. Running the example evaluates the Ridge Regression algorithm on the housing dataset and reports the average MAE across the three repeats of 10-fold cross-validation.
How to test / validate a regression model?
“To validate this one model, you can then use the data of your test set to find how well the model works (e.g.: how looks the distribution of errors).” – can you please explain this step in more detail? For each prediction of your test set you can calculate the error (difference between the predicted response and the actual response).
What’s the penalty for a ridge regression model?
One popular penalty is to penalize a model based on the sum of the squared coefficient values ( beta ). This is called an L2 penalty. An L2 penalty minimizes the size of all coefficients, although it prevents any coefficients from being removed from the model by allowing their value to become zero.