When working with R and Python for machine learning, you gain the benefit of not having to reinvent the wheel when it comes to algorithms. There is a library available to meet your specific needs — you just need to know which one to use. This table provides you with a listing of the libraries used for machine learning for both R and Python. When you want to perform any algorithm-related task, simply load the library needed for that task into your programming environment.
| Algorithm | Python implementation | R implementation |
| Adaboost | sklearn.ensemble.AdaBoostClassifier sklearn.ensemble.AdaBoostRegressor |
library(ada) : ada |
| Gradient Boosting | sklearn.ensemble.GradientBoostingClassifier sklearn.ensemble.GradientBoostingRegressor |
library(gbm) : gbm |
| K-means | sklearn.cluster.KMeans sklearn.cluster.MiniBatchKMeans |
library(stats) : kmeans |
| K-nearest Neighbors | sklearn.neighbors.KNeighborsClassifier sklearn.neighbors.KNeighborsRegressor |
library(class): knn |
| Linear regression | sklearn.linear_model.LinearRegression sklearn.linear_model.Ridge sklearn.linear_model.Lasso sklearn.linear_model.ElasticNet sklearn.linear_model.SGDRegressor |
library(stats) : lm library(stats) : glm library(MASS) : lm.ridge library(lars) : lars library(glmnet) : glmnet |
| Logistic regression | sklearn.linear_model.LogisticRegression sklearn.linear_model.SGDClassifier |
library(stats) : glm library(glmnet) : glmnet |
| Naive Bayes | sklearn.naive_bayes.GaussianNB sklearn.naive_bayes.MultinomialNB sklearn.naive_bayes.BernoulliNB |
library(klaR) : NaiveBayes library(e1071) : naiveBayes |
| Neural Networks | sklearn.neural_network.BernoulliRBM (in version 0.18 of Scikit-learn, a new implementation of supervised neural network will be introducted) |
library(neuralnet) : neuralnet library(AMORE) : train library(nnet) : nnet |
| PCA | sklearn.decomposition.PCA | library(stats): princomp library(stats) : stats |
| Random Forest | sklearn.ensemble.RandomForestClassifier sklearn.ensemble.RandomForestRegressor sklearn.ensemble.ExtraTreesClassifier sklearn.ensemble.ExtraTreesRegressor |
library(randomForest) : randomForest |
| Support Vector Machines | sklearn.svm.SVC sklearn.svm.LinearSVC sklearn.svm.NuSVC sklearn.svm.SVR sklearn.svm.LinearSVR sklearn.svm.NuSVR sklearn.svm.OneClassSVM |
library(e1071) : svm |
| SVD | sklearn.decomposition.TruncatedSVD sklearn.decomposition.NMF |
library(irlba) : irlba library(svd) : svd |
