from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import cross_val_score
import matplotlib.pyplot as plt
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4)
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
print(knn.score(X_test, y_test))
scores = cross_val_score(knn, X, y, cv=5, scoring="accuracy")
print(scores)
print(scores.mean())
k_range = range(1,31)
k_scores = []
for k in k_range:
knn = KNeighborsClassifier(n_neighbors=k)
# scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')
# k_scores.append(scores.mean())
loss = -cross_val_score(knn, X, y, cv=10, scoring='mean_squared_error')
k_scores.append(loss.mean())
plt.plot(k_range, k_scores)
plt.xlabel('Value of K for KNN')
plt.ylabel('Cross-Validated Accuracy')
plt.show()
https://morvanzhou.github.io/tutorials/machine-learning/sklearn/3-2-cross-validation1/
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