GridSearchCV

GridsearchCV is a method of tuning wherein the model can be built by evaluating the combination of parameters mentioned in a grid. In the following figure, we will see how GridSearchCV is different from manual search and look at grid search in a muchdetailed way in a table format.

Tuning using GridSearchCV

We can conduct a grid search much more easily in practice by leveraging model_selection.GridSearchCV.

For the sake of comparison, we will use the same breast cancer dataset and k-NN classifier as before:

from sklearn import model_selection, datasets, neighbors
# load the data
cancer = datasets.load_breast_cancer()
# target
y = cancer.target
# features
X = cancer.data

The next thing we need to do after loading the data is to initialize the class of the estimator we would like to evaluate under different hyperparameterizations:

# initialize the estimator
knn = neighbors.KNeighborsClassifier()

We then define the grid:

# grid contains k and the weight...