ML之LoR&SGD:基于LoR(逻辑回归)、SGD梯度下降算法对乳腺癌肿瘤(10+1)进行二分类预测(良/恶性)
目录
- breast-cancer size (683, 11)
-
- 训练集情况
- 2 344
- 4 168
- Name: Class, dtype: int64
-
- 测试集情况
- 2 100
- 4 71
- Name: Class, dtype: int64
- from sklearn.cross_validation import train_test_split
- X_train, X_test, y_train, y_test = train_test_split(data[column_names[1:10]], data[column_names[10]], test_size=0.25, random_state=33)
-
- ss = StandardScaler()
- X_train = ss.fit_transform(X_train)
- X_test = ss.transform(X_test)
-
- lr = LogisticRegression()
- sgdc = SGDClassifier()
-
- lr.fit(X_train, y_train)
- lr_y_predict = lr.predict(X_test)
-
- sgdc.fit(X_train, y_train)
- sgdc_y_predict = sgdc.predict(X_test)
-
- lr.score(X_test, y_test))
- sgdc.score(X_test, y_test))
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