- Start by importing the model from sklearn, followed by a balanced split:
from sklearn.neighbors import KNeighborsClassifierX_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state = 0)
The random_state parameter fixes the random_seed in the function train_test_split. In the preceding example, the random_state is set to zero and can be set to any integer.
- Construct two different KNN models by varying the n_neighbors parameter. Observe that the number of folds is now 10. Tenfold cross-validation is common in the machine learning community, particularly in data science competitions:
from sklearn.model_selection import cross_val_scoreknn_3_clf = KNeighborsClassifier(n_neighbors = 3)knn_5_clf = KNeighborsClassifier(n_neighbors ...
