# Kod 1
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
breast_cancer = load_breast_cancer()
X = pd.DataFrame(breast_cancer["data"],
columns = breast_cancer["feature_names"])
y = pd.Series(breast_cancer["target"])
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size = 0.25,
random_state = 42)
# Kod 2
knn = KNeighborsClassifier()
knn.fit(X = X_train, y = y_train)
train_score = knn.score(X = X_train, y = y_train)
test_score = knn.score(X = X_test, y = y_test)
train_score, test_score
(0.9342723004694836, 0.965034965034965)
%%time
# Kod 3
from sklearn.model_selection import GridSearchCV
param_grid = {'n_neighbors': range(1, 101),
'weights': ["uniform", "distance"],
'p': [1, 2]
}
knn_grid_search = GridSearchCV(estimator = KNeighborsClassifier(), param_grid = param_grid, cv = 5, iid = False)
knn_grid_search.fit(X = X_train, y = y_train)
grid_train_score = knn_grid_search.score(X = X_train, y = y_train)
grid_test_score = knn_grid_search.score(X = X_test, y = y_test)
print(grid_train_score, grid_test_score)
print(knn_grid_search.best_params_)
0.9436619718309859 0.972027972027972 {'n_neighbors': 6, 'p': 1, 'weights': 'uniform'} CPU times: user 1min 8s, sys: 164 ms, total: 1min 9s Wall time: 1min 9s