If you have run the previous experiment, you may have realized that:
Both the validation and test results vary as samples are different
The chosen hypothesis is often the best one, but this is not always the case
Unfortunately, relying on the validation and testing phases of samples brings uncertainty along with a strong reduction of the learning examples for training (the fewer the examples, the more the variance of the obtained model).
A solution is to use cross-validation, and Scikit-learn offers a complete module for cross-validation and performance evaluation (sklearn.cross_validation).
By resorting to cross-validation, you'll just need to separate your data into a training and test set, and you will be able to use the training data for both model optimization and model training.
How does cross-validation work? The idea is to divide your training data into a certain number of partitions (called folds) and train your model as many times as the number of partitions, keeping...