Machine Learning For Dummies
Book image
Explore Book Buy On Amazon
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

About This Article

This article is from the book:

About the book authors:

John Paul Mueller is a prolific freelance author and technical editor. He's covered everything from networking and home security to database management and heads-down programming.

Luca Massaron is a data scientist who specializes in organizing and interpreting big data, turning it into smart data with data mining and machine learning techniques.

This article can be found in the category: