# 7.5: Deep Learning

- Page ID
- 3586

Nowadays, “deep learning” is a bit of buzzword which used to designate software packages including multiple classification methods, and among the always some complicated neural networks (multi-layered, recurrent etc.) In that sense, R with necessary packages **is** a deep learning system. What is missed (actually, not), is a common interface to all “animals” in this zoo of methods. Package mlr was created to unify the learning interface in R:

**Code \(\PageIndex{1}\) (Python):**

library(mlr) ... ## 1) Define the task ## Specify the type of analysis (e.g. classification) ## and provide data and response variable task <- makeClassifTask(data=iris, target="Species") ## 2) Define the learner, use listLearners()[,1] ## Choose a specific algorithm lrn <- makeLearner("classif.ctree") n = nrow(iris) train.set <- sample(n, size=2/3*n) test.set <- setdiff(1:n, train.set) ## 3) Fit the model ## Train the learner on the task using a random subset ## of the data as training set model <- train(lrn, task, subset=train.set) ## 4) Make predictions ## Predict values of the response variable for new ## observations by the trained model ## using the other part of the data as test set pred <- predict(model, task=task, subset=test.set) ## 5) Evaluate the learner ## Calculate the mean misclassification error and accuracy performance(pred, measures=list(mmce, acc))

In addition, R now has interfaces (ways to connect with) to (almost) all famous “deep learning” software systems, namely TensorFlow, H2O, Keras, Caffe and MXNet.