I am using sklearn to train two models and compare their outcome with each other but I am not sure how to evaluate the models. As I have little data (approx. 300 data points) I want to use cross validation to train my model. So I have read that it is only used for hyper parameter tuning which I am already doing with GridSearchCV.

To train my model with more data I would want to use cross validation as well for training purposes. Does it make sense to do so?

My division of data would look like this:

  1. 80% of data: train/validation with cross validation and hyperparameter tuning (so the model is trained as well already in this step)
  2. 20% of data: test set and measuring MAE and RMSE as well as analysing prediction made by trained model

or should I do it that way before using GridSearch:

Split data into three data chunks:

  1. 60% of data for training
  2. 20% of data for testing
  3. 20% of data for validation

First step: Use the validation set in GridSearch and find the best parameters based on validation.

Second step: Train model with best parameters found by grid search with training set

Third step: Test model and calculate MAE and RSME on test set and as well as analysing prediction made by trained model

If both evaluation methods are not right, I would be grateful for any tips how to find the best way to evaluate my models.

  1. Divide your data into training set and test set (e.g. 80%, 20%).
  2. Perform the Grid Search (which uses k-fold cross validation) on the training set for two models and pick best parameters. GridSearchCV will divide the training set into $k$ train/validate splits on its own.
  3. Use the test set to compare the final models.

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