python机器学习—— 数据预处理 & 算法初步

本是古典何须时尚 2023-10-12 20:24 13阅读 0赞

#### 目录 ####

*  数据预处理
 *  1.获取数据
 *  2.处理缺失值
 *  3.划分数据集
 *  4.数据预处理和PCA降维
 *  5.算法实现：估计器

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![在这里插入图片描述][5e4f7fd9d0c846ae82a3cd55eb2b5537.png]

## 数据预处理 ##

## 1.获取数据 ##

![在这里插入图片描述][bbd7d8bd2a9e4f2ea7ec19c03011cf9d.png]  
![在这里插入图片描述][37509c8a8a844bfaa37bd59fba600316.png]

from sklearn.datasets import load_iris
    li=load_iris()
    print("获取特征值")
    print(li.data)
    print("目标值",li.target)#分类数据集

用于分类的大数据集

![在这里插入图片描述][83236ab7e8cc458b8d23fcf8d7a8fc68.png]

from sklearn.datasets import load_iris,fetch_20newsgroups
    news=fetch_20newsgroups(subset="all")
    print(news.data)
    print(news.target)

from sklearn.datasets import load_boston
    lb=load_boston()
    print(lb.data)
    print(lb.target)#回归数据集

## 2.处理缺失值 ##

![在这里插入图片描述][689c96180a864d9c8de96789f9d7d634.png]  
![在这里插入图片描述][f0537a793109421e990a44f7f55348bc.png]

## 3.划分数据集 ##

训练集与测试集划分：70% 30%；80% 20%；75% 25%  
![在这里插入图片描述][4e9562d9e0f14932bf087e5ab052a1d2.png]

from sklearn.datasets import load_breast_cancer
    cancer = load_breast_cancer()##将数据集赋值给cancer变量
    cancer_data = cancer['data']
    cancer_target = cancer['target']
    from sklearn.model_selection import train_test_split
    cancer_data_train, cancer_data_test,cancer_target_train, cancer_target_test = \
    train_test_split(cancer_data, cancer_target,test_size=0.2, random_state=42) # test_size表示测试集在总数中的占比

## 4.数据预处理和PCA降维 ##

![在这里插入图片描述][ffe147eb27a9409eabd3e5aee35e2974.png]

#离差标准化
    from sklearn.preprocessing import MinMaxScaler
    Scaler = MinMaxScaler().fit(cancer_data_train) ##生成规则
    ##将规则应用于训练集
    cancer_trainScaler = Scaler.transform(cancer_data_train) 
    ##将规则应用于测试集
    cancer_testScaler = Scaler.transform(cancer_data_test)

![在这里插入图片描述][ee970cffe6894aecb88df5a0a098bca5.png]

from sklearn.decomposition import PCA
    pca_model = PCA(n_components=10).fit(cancer_trainScaler) ##生成规则
    cancer_trainPca = pca_model.transform(cancer_trainScaler) ##将规则应用于训练集
    cancer_testPca = pca_model.transform(cancer_testScaler) ##将规则应用于测试集

## 5.算法实现：估计器 ##

![在这里插入图片描述][6afb9eba71c14683ac9a14176fec1af7.png]  
![在这里插入图片描述][6fc4a2881c324e57b580303c93a66186.png]

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