应用逻辑回归与SVM

Python 中的线性分类器

Michael (Mike) Gelbart

Instructor, The University of British Columbia

使用 LogisticRegression

from sklearn.linear_model import LogisticRegression

lr = LogisticRegression()

lr.fit(X_train, y_train)

lr.predict(X_test)

lr.score(X_test, y_test)

LogisticRegression 示例

import sklearn.datasets
wine = sklearn.datasets.load_wine()

from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(wine.data, wine.target)

lr.score(wine.data, wine.target)

0.966

lr.predict_proba(wine.data[:1])

array([[9.966e-01, 2.740e-03, 6.787e-04]])

使用 LinearSVC

LinearSVC 的用法相同：

import sklearn.datasets

wine = sklearn.datasets.load_wine()

from sklearn.svm import LinearSVC

svm = LinearSVC()

svm.fit(wine.data, wine.target)

svm.score(wine.data, wine.target)

0.955

使用 SVC

import sklearn.datasets
wine = sklearn.datasets.load_wine()

from sklearn.svm import SVC
svm = SVC()
svm.fit(wine.data, wine.target);

svm.score(wine.data, wine.target)

0.708

模型复杂度回顾：

低拟合：模型过于简单，训练准确率低
过拟合：模型过于复杂，测试准确率低

让我们练习吧！

Python 中的线性分类器