邏輯回歸模型 乳腺癌資料集

# 匯入資料集

from sklearn import datasets
import warnings
warnings.filterwarnings(
'ignore'
)df = datasets.load_breast_cancer(
)x = df.data
y = df.target
x.shape    # 檢視屬性維度
x  # 檢視屬性標籤

y  # 檢視類別標籤

# 劃分訓練集和測試集

from sklearn.linear_model import logisticregression    # 匯入邏輯回歸模型

from sklearn.preprocessing import standardscaler     # 歸一化
from sklearn.preprocessing import polynomialfeatures    # 生成多項式
from sklearn.pipeline import pipeline      # pipeline管道
# 直接用邏輯回歸模型進行訓練
logr = logisticregression(
)logr.fit(x_train, y_train)
# 訓練
logr.score(x_test, y_test)
# 測試
from sklearn.model_selection import gridsearchcv    # 網格搜尋

import numpy as np
# 使用網格搜尋和pipeline管道進行引數調優
對於模型的調優，由於時間關係，大家可以根據邏輯回歸模型和polynomialfeatures的引數進行調優。
				
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