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  • 机器学习—Logistic Regression

    一、一般模型

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn import metrics
    from sklearn.datasets import load_iris
    %matplotlib inline
    #载入数据
    iris = load_iris()
    x = iris.data
    y = iris.target
    x_train,x_test,y_train,y_test = train_test_split(x,y,train_size=0.7,random_state=0)
    #数据标准化
    sc = StandardScaler()
    x_train_std = sc.fit_transform(x_train)
    x_test_std = sc.transform(x_test)
    #建立模型
    lr = LogisticRegression()
    lr.fit(x_train_std,y_train)
    y_pred = lr.predict(x_test_std)
    #检验模型
    accuracy_score = metrics.accuracy_score(y_test,y_pred)   #错误率,也就是np.average(y_test==y_pred)
    accuracy_score

    结果是:0.82222222222222219

    二、加入正则项:

    from sklearn.linear_model import RidgeClassifierCV
    alpha = np.logspace(-3,2,10)
    ridge_model = RidgeClassifierCV(alphas=alpha,cv=5)
    ridge_model.fit(x_train_std,y_train)
    ridge_model.alpha_
    y_pred_ridge = ridge_model.predict(x_test_std)
    accuracy_score = metrics.accuracy_score(y_test,y_pred_ridge)
    accuracy_score

    结果是:0.77777777777777779

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  • 原文地址:https://www.cnblogs.com/jiegege/p/7629992.html
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