机器学习模型总结（sklearn）
需积分: 42 190 浏览量
2018-03-16
12:10:49
上传
评论 5
收藏 175KB PDF 举报
1.监督学习及相关模型
监督学习
模型名称
API
分类
LogisticRegression(逻辑回归)
from sklearn.linear_model import LogisticRegression
SGDClassifier(随机梯度下降分类器)
from sklearn.linear_model import SGDClassifier
SVC(支持向量机分类器)
from sklearn.svm import LinearSVC
Naive Bayes(朴素贝叶斯)
from sklearn.naive_bayes import MultinomialNB
KNN(K 近邻分类器)
from sklearn.neighbors import KNeighborsClassifier
DecisionTree(决策树)
from sklearn.tree import DecisionTreeClassifier
Ensemble
(集成分类模型)
RandomForestClassifier
(随机森林)
from sklearn.ensemble import RandomForestClassifier
GradientBoostingClassifier
(梯度提升决策树)
from sklearn.ensemble import GradientBoostingClassifier
回归
LinearRegression(线性回归)
from sklearn.linear_model import LinearRegression
SGDRegressor(随机梯度下降回归器)
from sklearn.linear_model import SGDRegressor
SVR(支持向量机回归器)
from sklearn.svm import SVR
KNN(K 近邻回归器)
from sklearn.neighbors import KNeighborRegressor
DecisionTreeRegressor(回归树)
from sklearn.tree import DecisionTreeRegressor
Ensemble
(集成回归模型)
RandomForestRegressor
(随机回归森林)
from sklearn.ensemble import RandomForestRegressor
ExtraTreesRegressor
(极端回归森林)
from sklearn.ensemble import ExtraTreesRegressor
GradientBoostingRegressor
(梯度提升回归树)
from sklearn.ensemble import GradientBoostingRegressor
性能评估
场景
评估方法
API
分类
Accuracy(准确度)
estimator.score(X_test,y_test)
精确率、召回率、F1 分数
from sklearn.metrics import Classification_report
回归
R_squared(模型自带)
estimator.score(X_test,y_test)
R_squared,
MSE(平方相对误差),
MAE(平方绝对误差)
from sklearn.metrics import
r2_score,
mean_squared_error,
mean_absolute_error
常用模型
应用场景
资源评论
lamesoeur
- 粉丝: 10
- 资源: 2
