吴裕雄 python 机器学习——模型选择验证曲线validation_curve模型

import numpy as np

import matplotlib.pyplot as plt

from sklearn.svm import LinearSVC

from sklearn.datasets import load_digits

from sklearn.model_selection import validation_curve

#模型选择验证曲线validation_curve模型

def test_validation_curve():

    '''

    测试 validation_curve 的用法 。验证对于 LinearSVC 分类器 ， C 参数对于预测准确率的影响

    '''

    ### 加载数据

    digits = load_digits()

    X,y=digits.data,digits.target

    #### 获取验证曲线 ######

    param_name="C"

    param_range = np.logspace(-2, 2)

    train_scores, test_scores = validation_curve(LinearSVC(), X, y, param_name=param_name,param_range=param_range,cv=10, scoring="accuracy")

    ###### 对每个 C ，获取 10 折交叉上的预测得分上的均值和方差 #####

    train_scores_mean = np.mean(train_scores, axis=1)

    train_scores_std = np.std(train_scores, axis=1)

    test_scores_mean = np.mean(test_scores, axis=1)

    test_scores_std = np.std(test_scores, axis=1)

    ####### 绘图 ######

    fig=plt.figure()

    ax=fig.add_subplot(1,1,1)

    ax.semilogx(param_range, train_scores_mean, label="Training Accuracy", color="r")

    ax.fill_between(param_range, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.2, color="r")

    ax.semilogx(param_range, test_scores_mean, label="Testing Accuracy", color="g")

    ax.fill_between(param_range, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.2, color="g")

    ax.set_title("Validation Curve with LinearSVC")

    ax.set_xlabel("C")

    ax.set_ylabel("Score")

    ax.set_ylim(0,1.1)

    ax.legend(loc='best')

    plt.show()

#调用test_validation_curve()

test_validation_curve()