Randomized search with scikit-learn

From a practical standpoint, RandomizedSearchCV is more important than a regular grid search. This is because with a medium amount of data, or with a model involving a few parameters, it is too computationally expensive to try every parameter combination involved in a complete grid search.

Computational resources are probably better spent stratifying sampling very well, or improving randomization procedures.

Getting ready

As before, load the last two features of the iris dataset. Split the data into training and testing sets:

from sklearn import datasets

iris = datasets.load_iris()
X = iris.data[:,2:]
y = iris.target

from sklearn.model_selection import train_test_split

X_train, X_test, y_train...