莫烦python教程学习笔记——利用交叉验证计算模型得分、选择模型参数

# View more python learning tutorial on my Youtube and Youku channel!!!# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
# Youku video tutorial: http://i.youku.com/pythontutorial"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
from __future__ import print_function
from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifieriris = load_iris()
X = iris.data
y = iris.target# test train split #
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
print(knn.score(X_test, y_test))# this is cross_val_score # 计算模型得分
from sklearn.cross_validation import cross_val_score
knn = KNeighborsClassifier(n_neighbors=5)
scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')
print(scores)# this is how to use cross_val_score to choose model and configs # 选择模型参数
from sklearn.cross_validation import cross_val_score
import matplotlib.pyplot as plt
k_range = range(1, 31)
k_scores = []
for k in k_range:
knn = KNeighborsClassifier(n_neighbors=k)
## loss = -cross_val_score(knn, X, y, cv=10, scoring='mean_squared_error') # for regression
scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy') # for classification
k_scores.append(scores.mean())plt.plot(k_range, k_scores)
plt.xlabel('Value of K for KNN')
plt.ylabel('Cross-Validated Accuracy')
plt.show()