July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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263
第 16 章
逻辑回归
16.0
简介
尽管名字里有“回归”两个字，但逻辑回归其实是一个被广泛使用的有监督分类方法。
逻辑回归和它的一些扩展（比如多元逻辑回归）让我们可以通过一个简单易懂的方法来
预测一个观察值属于某个分类的概率。本章将讲解如何使用
scikit-learn
来训练各种分类
器。
16.1
训练二元分类器
问题描述
训练一个简单的二元分类器模型。
解决方案
使用
scikit-learn
的
LogisticRegression
训练一个逻辑回归模型 ：
#
加载库
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
#
加载仅有两个分类的数据
iris = datasets.load_iris()
features = iris.data[:100,:]
target = iris.target[:100]
264
｜
第
16
章
逻辑回归
#
标准化特征
scaler = StandardScaler()
features_standardized = scaler.fit_transform(features) ...
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