ML之LoR:基于LoR(逻辑回归)算法对乳腺癌肿瘤(9+1)进行二分类预测(良/恶性)

输出结果





Testing accuracy (10 training samples): 0.8685714285714285
Testing accuracy (all training samples): 0.9371428571428572

 

 

设计思路



数据集



       
 可知该原始数据共有699条样本,每条样本有11列不同的数值:1列用于检索的id,9列与肿瘤相关的医学特征,以及一列表征肿瘤类型的数值。所有9列用于表示肿瘤医学特质的数值均被量化为1~10之间的数字,而肿瘤的类型也借由数字2和数字4分别指代良性与恶性。不过,这份数据也声明其中包含16个缺失值,并且用“?”标出。本代码中,对于存在缺失值的数据,都暂时子以忽略。

Number of Instances:699 (as of 15 July 1992)
Number of Attributes:10 plus the class attribute
Attribute Information:(class attribute has been moved to last column

#Attribute                             Domain                               
1 .Sample code number         id number
2 .Clump Thickness                  1一10
3 .Uniformity of Cell                  1一10
4 .Uniformity of Cell                  1一10
5 .Marginal Adhesion                1一10
6 .Single Epithelial Cell Size     1一10
7 .Bare Nuclei                           1一10
8 .Bland Chromatin                   1一10
9. Normal Nucleoli                    1一10
10 .Mitoses                               1一10
11 .Class:               (2 for benign,  4 for malignant)

 

核心代码
import numpy as np intercept = np.random.random([1]) coef =
np.random.random([2]) lx = np.arange(0, 12) ly = (-intercept - lx * coef[0]) /
coef[1] from sklearn.linear_model import LogisticRegression lr =
LogisticRegression() lr.fit(df_train[['Clump Thickness', 'Cell Size']][:10],
df_train['Type'][:10]) print('Testing accuracy (10 training samples):',
lr.score(df_test[['Clump Thickness', 'Cell Size']], df_test['Type'])) intercept
= lr.intercept_ coef = lr.coef_[0, :] ly = (-intercept - lx * coef[0]) /
coef[1] lr = LogisticRegression() lr.fit(df_train[['Clump Thickness', 'Cell
Size']], df_train['Type']) print('Testing accuracy (all training samples):',
lr.score(df_test[['Clump Thickness', 'Cell Size']], df_test['Type'])) intercept
= lr.intercept_ coef = lr.coef_[0, :] ly = (-intercept - lx * coef[0]) / coef[1]
 

 

 

 

 

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