Python和R代码机器学习算法速查对比表.docx

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1、Python和R代码机器学习算法速查对比表这里给出的速查表收集了10个最为常用的机器学习算法，附上了Python和R代码。考虑到机器学习方法在建模中得到了更多的运用，以下速查表可以作为代码指南来帮助你掌握机器学习算法运用。机器学习算法类 型监督学习非监督学习增强学习决策树K-近邻算法随机决策森林Logistics回归分析Apriori算法K-均值算法系统聚类马尔科夫决策过程增强学习算法（Q-学习）线性回归#Import Library#Import other necessary libraries like pandas,#numpy.from sklearn import linear_m

2、odel#Load Train and Test datasets#Identify feature and response variable(s) and#values must be numeric and numpy arraysx_train=input_variables_values_training_datasets y_train=target_variables_values_training_datasets x_test=input_variables_values_test_datasets#Create linear regression objectlinear

3、= linear_model.LinearRegression()#Train the model using the training sets and #check scorelinear.fit(x_train, y_train) linear.score(x_train, y_train)#Equation coefficient and Intercept print(Coefficient: n, linear.coef_) print(Intercept: n, linear.intercept_) #Predict Outputpredicted= linear.predict

4、(x_test)#Load Train and Test datasets#Identify feature and response variable(s) and#values must be numeric and numpy arraysx_train - input_variables_values_training_datasetsy_train - target_variables_values_training_datasetsx_test - input_variables_values_test_datasetsx - cbind(x_train,y_train)#Trai

5、n the model using the training sets and#check scorelinear - lm(y_train ., data = x)summary(linear)#Predict Outputpredicted= predict(linear,x_test)逻辑回归#Import Libraryfrom sklearn.linear_model import LogisticRegression#Assumed you have, X (predictor) and Y (target)#for training data set and x_test(pre

6、dictor)#of test_dataset#Create logistic regression objectmodel = LogisticRegression()#Train the model using the training sets#and check scoremodel.fit(X, y)model.score(X, y)#Equation coefficient and Interceptprint(Coefficient: n, model.coef_)print(Intercept: n, model.intercept_)#Predict Outputpredic

7、ted= model.predict(x_test)x - cbind(x_train,y_train)#Train the model using the training sets and check #scorelogistic - glm(y_train ., data = x,family=binomial) summary(logistic)#Predict Outputpredicted= predict(logistic,x_test)决策树#Import Library#Import other necessary libraries like pandas, numpy.

8、from sklearn import tree#Assumed you have, X (predictor) and Y (target) for#training data set and x_test(predictor) of #test_dataset#Create tree objectmodel = tree.DecisionTreeClassifier(criterion=gini) #for classification, here you can change the #algorithm as gini or entropy (information gain) by#

9、default it is gin#model = tree.DecisionTreeRegressor() for#regression#Train the model using the training sets and check #scoremodel.fit(X, y)model.score(X, y)#Predict Outputpredicted= model.predict(x_test)#Import Librarylibrary(rpart)x -cbind(x_train,y_train)#grow treefit - rpart(y_train ., data = x

10、,method=class) summary(fit)#Predict Outputpredicted= predict(fit,x_test)支持向量机#Import Libraryfrom sklearn import svm#Assumed you have, X (predictor) and Y (target) for #training data set and x_test(predictor) of test_dataset#Create SVM classification objectmodel = svm.svc()#there are various options

11、associatedwith it, this is simple for classification.#Train the model using the training sets and check #scoremodel.fit(X, y)model.score(X, y)#Predict Outputpredicted= model.predict(x_test)#Import Librarylibrary(e1071)x - cbind(x_train,y_train) #Fitting modelfit -svm(y_train ., data = x) summary(fit

12、)#Predict Outputpredicted= predict(fit,x_test)贝叶斯算法#Import Libraryfrom sklearn.naive_bayes import GaussianNB#Assumed you have, X (predictor) and Y (target) for#training data set and x_test(predictor) of test_dataset#Create SVM classification object model = GaussianNB()#there is other distribution fo

13、r multinomial classes like Bernoulli Naive Bayes#Train the model using the training sets and check#scoremodel.fit(X, y)#Predict Outputpredicted= model.predict(x_test)#Import Librarylibrary(e1071)x - cbind(x_train,y_train)#Fitting modelfit -naiveBayes(y_train ., data = x) summary(fit)#Predict Outputp

14、redicted= predict(fit,x_test)k-近邻算法#Import Libraryfrom sklearn.neighbors import KNeighborsClassifier#Assumed you have, X (predictor) and Y (target) for#training data set and x_test(predictor) of test_dataset#Create KNeighbors classifier object model KNeighborsClassifier(n_neighbors=6)#default value

15、for n_neighbors is 5#Train the model using the training sets and check score model.fit(X, y)#Predict Outputpredicted= model.predict(x_test)#Import Librarylibrary(knn)x - cbind(x_train,y_train)#Fitting modelfit -knn(y_train ., data = x,k=5) summary(fit)#Predict Outputpredicted= predict(fit,x_test)Pyt

16、hon R硬聚类算法#Import Libraryfrom sklearn.cluster import KMeans#Assumed you have, X (attributes) for training data set#and x_test(attributes) of test_dataset#Create KNeighbors classifier object modelk_means = KMeans(n_clusters=3, random_state=0)#Train the model using the training sets and check score mo

17、del.fit(X)#Predict Outputpredicted= model.predict(x_test)#Import Librarylibrary(cluster)fit - kmeans(X, 3)#5 cluster solution随机森林算法#Import Libraryfrom sklearn.ensemble import RandomForestClassifier#Assumed you have, X (predictor) and Y (target) for#training data set and x_test(predictor) of test_dat

18、aset#Create Random Forest objectmodel= RandomForestClassifier()#Train the model using the training sets and check score model.fit(X, y)#Predict Outputpredicted= model.predict(x_test)#Import Librarylibrary(randomForest)x - cbind(x_train,y_train)#Fitting modelfit - randomForest(Species ., x,ntree=500)

19、 summary(fit)#Predict Outputpredicted= predict(fit,x_test)降维算法#Import Libraryfrom sklearn import decomposition#Assumed you have training and test data set as train and#test#Create PCA object pca= decomposition.PCA(n_components=k) #default value of k =min(n_sample, n_features)#For Factor analysis#fa=

20、 decomposition.FactorAnalysis()#Reduced the dimension of training dataset using PCA train_reduced = pca.fit_transform(train)#Reduced the dimension of test datasettest_reduced = pca.transform(test)#Import Librarylibrary(stats)pca - princomp(train, cor = TRUE)train_reduced - predict(pca,train)test_red

21、uced - predict(pca,test)GBDT#Import Libraryfrom sklearn.ensemble import GradientBoostingClassifier#Assumed you have, X (predictor) and Y (target) for#training data set and x_test(predictor) of test_dataset#Create Gradient Boosting Classifier objectmodel= GradientBoostingClassifier(n_estimators=100,

22、learning_rate=1.0, max_depth=1, random_state=0)#Train the model using the training sets and check score model.fit(X, y)#Predict Outputpredicted= model.predict(x_test)#Import Librarylibrary(caret)x - cbind(x_train,y_train)#Fitting modelfitControl - trainControl( method = repeatedcv, + number = 4, repeats = 4)fit - train(y ., data = x, method = gbm,+ trControl = fitControl,verbose = FALSE)predicted= predict(fit,x_test,type= prob),2