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

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ML之LoR：基于LoR(逻辑回归)算法对乳腺癌肿瘤进行二分类预测(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

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]