监督学习,supervised learning
无监督学习,unsupervised learning
分类,classificat
回归,regression
降维,dimensionality reduction
聚类,clustering
特征向量,feature vector
编译语言,complied languages
解释型语言,interpreted languages
解释器,interpreter
布尔值,boolean
元组,tuple
算术运算,arithmetic operators
比较运算,comparison operators
赋值运算,assignment operators
逻辑运算,logical operators
成员运算,menbership operators
二分类,binary classification
多分类,multiclass classification
多标签分类,multi-lable classification
线性分类器,linear classification
系数,coefficient
截距,intercept
参数,parameters
随机梯度上升,stochastic gradient ascend(SGA)
预测结果,predicted condition
正确标记,true condition
混淆矩阵,confusion matrix
准确性,accuracy
召回率,recall
精确率,precision
随机梯度下降模型,SGD Classifier
支持向量机分类器,support vector classifier
朴素贝叶斯,naive bayes
K近邻分类器,KNeighborsClassifier
无参数模型,nonparametric model
信息熵,information gain
基尼不纯性,gini impurity
集成,ensemble
单一决策树,decision tree
随机森林分类器,random forest classifier
梯度提升决策树,gradient tree boosting
平均绝对误差,mean absolute error(MAE)
均方误差,mean squared error(MSE)
极端随机森林,extremely randomized trees
随机回归森林,randomforestregressor
极端回归森林,extratreesregressor
核函数,kernal
scikit-learn
针对房价预测的回归预测能力排名,R-squared(用来衡量模型回归结果的波动可被真实值验证的百分比，也暗示了模型在数值回归方面的能力）
1,gradient boosting regressor,0.8426
2,extra trees regressor,0.8195
3,random forest regressor,0.8024
4,SVM regressor(RBF kernel),0.7564
5,KNN regressor(distance-weighted),0.7198
6,decision tree regressor,0.6941
7,KNN regressor(uniform-weighted),0.6903
8,linear regressor,0.6763
9,SGDregressor,0.6599
10,SVM regressor(linear kernel),0.6517
11,SVM regressor(poly kernel),0.4045
泛化力,generalization
正则化,regularization
过拟合,overfitting
留一验证,leave-one-out cross validation
交叉验证,K-flod cross-validation