At the beginning of the model selection and cross-validation chapter we tried to select the best nearest-neighbor model for the two last features of the iris dataset. We will refocus on that now with GridSearchCV in scikit-learn.
Grid search with scikit-learn
Getting ready
First, 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, cross_val_score
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y,random_state = 7)
