GridSearchCV交叉驗證

代碼實現（基於邏輯回歸算法）：

# -*- coding: utf-8 -*-
"""
Created on Sat Sep  1 11:54:48 2018

@author: zhen

    交叉驗證
"""
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt

iris = datasets.load_iris()
x = iris['data'][:, 3:]
y = iris['target']


def report(results, n_top=3):
    for i in range(1, n_top + 1):
        candidates = np.flatnonzero(results['rank_test_score'] == i)
        for candidate in candidates:
            print("Model with rank: {0}".format(i))
            print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
                   results['mean_test_score'][candidate],
                   results['std_test_score'][candidate]))
            print("Parameters: {0}".format(results['params'][candidate]))
            print("")


param_grid = {"tol":[1e-4, 1e-3,1e-2], "C":[0.4, 0.6, 0.8]}

log_reg = LogisticRegression(multi_class='ovr', solver='sag')
# 采用3折交叉驗證
grid_search = GridSearchCV(log_reg, param_grid=param_grid, cv=3)
grid_search.fit(x, y)

report(grid_search.cv_results_)

x_new = np.linspace(0, 3, 1000).reshape(-1, 1)
y_proba = grid_search.predict_proba(x_new)
y_hat = grid_search.predict(x_new)

plt.plot(x_new, y_proba[:, 2], 'g-', label='Iris-Virginica')
plt.plot(x_new, y_proba[:, 1], 'r-', label='Iris-Versicolour')
plt.plot(x_new, y_proba[:, 0], 'b-', label='Iris-Setosa')
plt.show()

print(grid_search.predict([[1.7], [1.5]]))

結果：

總結：使用交叉驗證可以實現代碼自動對設定范圍參數的模型進行分別訓練，最后選出效果最好的參數所訓練出的模型進行預測，以求達到最好的預測效果！