逻辑回归API介绍


sklearn.linear_model.LogisticRegression(solver-'liblinear', penalty='12', C=1.0)solver可选参数：('liblinear', 'sag', 'saga','newton-cg', 'Ibfgs')，默认：'liblinear'：用于优化问题的算法。对于小数据集来说，“liblinear”是个不错的选择，而“sag”和'saga'对于大型数据集会更快。对于多类问题，只有'newton-cg'，'sag'，'saga'和'lbfgs'可以处理多项损失；olinear”仅限于“one-versus-rest”分类。penalty:正则化的种类C:正则化力度默认将类别数量少的当做正例LogisticRegression方法相当于 SGDClassifier(loss="log",penalty="")、SGDClassifier实现了一个普通的随机梯度下降学习，而使用LogisticRegression（实现了SAG）。

逻辑回归API使用

使用【威斯康星州乳腺癌症数据集】，预测癌症分类，采用逻辑回归分析算法

数据集下载位置：UCI Machine Learning Repository

import joblib
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScalercols = ['Sample-code-number', 'Clump-Thickness', 'Uniformity-of-Cell-Size',  'Uniformity-of-Cell-Shape', 'Marginal-Adhesion', 'Single-Epithelial-Cell-Size',  'Bare-Nuclei', 'Bland-Chromatin', 'Normal-Nucleoli', 'Mitoses', 'Class']bcw = pd.read_csv('./data/breast-cancer-wisconsin-original/breast-cancer-wisconsin.data', names=cols)
bcw = bcw.replace(to_replace='?', value=np.NaN)
bcw = bcw.dropna()x = bcw.iloc[:, 1:10]
y = bcw['Class']# 数据分割x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=10)# 标准化transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.fit_transform(x_test)# 逻辑回归estimator = LogisticRegression()
estimator.fit(x_train, y_train)print("score:\n", estimator.score(x_test, y_test))# 保存估计器
joblib.dump(value=estimator, filename='./data/breast-cancer-wisconsin-original/bcw.pkl')

运行结果：