机器学习之逻辑回归（三）：基于逻辑回归的癌症预测案例——【癌症分类预测-良／恶性乳腺癌肿瘤预测】

  * 2.2 数据基本处理 缺失值处理，划分数据集
  * * 2.2.1 ？标志的缺失值
* 2.2.2 划分数据集

  * 2.3 特征工程 标准化
  * 2.4 逻辑回归机器学习建模
  * * 2.4.1 建立模型
* 2.4.2 训练模型

  * 2.5 模型评估
  * * 2.5.1 计算准确率
* 2.5.2 其他的评估方法

* 3 代码

    import pandas as pd 
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LogisticRegression

    data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data')

    data.head()

    #给列名字
    names = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape','Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin','Normal Nucleoli', 'Mitoses', 'Class']

    #给data增加一个names参数
    data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data',names = names)

    data.head()

    data.Class#说明：2表示良性，4表示恶性

    #替换缺失值
    data = data.replace(to_replace='?',value=np.nan)

    ## 删除缺失值的样本
    data = data.dropna() #删除有np.nan的行

    #特征值
    x = data.iloc[:,1:10]
    x.head()

    #目标值
    y = data['Class']
    y.head()

    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)

    transform = StandardScaler()#实例化转换器
    #标准化
    x_train = transform.fit_transform(x_train)
    x_test = transform.fit_transform(x_test)

    estimate = LogisticRegression()#用默认的就行

    estimate.fit(x_train,y_train)#得到了模型

    estimate.score(x_train,y_train)

    estimate.score(x_test,y_test)

    import pandas as pd
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LogisticRegression
    
    import ssl
    ssl._create_default_https_context = ssl._create_unverified_context
    # 1.获取数据
    names = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape',
                   'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',
                   'Normal Nucleoli', 'Mitoses', 'Class']
    
    data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data",
                  names=names)
    data.head()
    # 2.基本数据处理
    # 2.1 缺失值处理
    data = data.replace(to_replace="?", value=np.NaN)
    data = data.dropna()
    # 2.2 确定特征值,目标值
    x = data.iloc[:, 1:10]
    x.head()
    y = data["Class"]
    y.head()
    # 2.3 分割数据
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=22)
    # 3.特征工程(标准化)
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)
    # 4.机器学习(逻辑回归)
    estimator = LogisticRegression()
    estimator.fit(x_train, y_train)
    # 5.模型评估
    y_predict = estimator.predict(x_test)
    y_predict
    estimator.score(x_test, y_test)