This post will explain us, how to install R on windows environment and how to work with Machine learning project using R with simple dataset
First Download R https://cran.r-project.org/bin/windows/base/ from this link.
You can download latest version of R.
1. Once download completes, then install the same in your machine. This is like any other software installation. There is no special instructions required for this.
2. After successful installation we need to setup the path- go to MyComputer-RightClick- environment variables- System variables-
C:\Program Files\R\R-3.5.1\bin
4. Go to command prompt and Type R
5. Now we can see the simple R terminal
7. When we are applying machine learning to our own datasets, we are working on a project.
The process of a machine learning project may not be linear, but there are a number of well-known steps:
Define Problem.
Prepare Data.
Evaluate Algorithms.
Improve Results.
Present Results.
8.The best way to really come in terms with a new platform or tool is to work through a machine learning project end-to-end and cover the key steps.
Namely, from loading data, summarizing your data, evaluating algorithms and making some predictions.
Machine Learning using R Step By Step
Now this the time to work simple machine learning program using R and inbuilt dataset called iris
We already installed R and it has started.
Install any default packages using following syntax.
Packages are third party add-ons or libraries that we can use in R.
install.packages("caret")
//While installing the package, after typing the above command , it will ask us for select mirror, you can select default one.
install.packages(“caret”,dependencies=c(“Depends”,”Suggests”))
install.packages(“ellipse”)
//Load the package, which we are going to use.
libray(caret)
// Attach iris dataset to the current environment data(iris) // Rename iris dataset to dataset dataset <- irisNow iris data loaded in R and accessible with variable called dataset Now we will create validation dataset. We will split the loaded dataset into two, 80% of which we will use to train our models and 20% that we will hold back as a validation dataset.
// We create a list of 80% of the rows in the original dataset we can use for training
validation_index <- createDataPartition(dataset$Species, p=0.80, list=FALSE)
//select 20% of the data for validation
validation <- dataset[-validation_index,]
//use the remaining 80% of data to training and testing the models
dataset <- dataset[validation_index,]
Now we have training data in the dataset variable and a validation set we will use later in the validation variable.
Note that we replaced our dataset variable with the 80% sample of the dataset.
1. dim function
We can get a quick idea of how many instances (rows) and how many attributes (columns) the data contains with the dim function.
dim(dataset)2. Attribute types - Knowing the types is important as it will give us an idea of how to better summarize the data we have and the types of transforms we might need to use to prepare the data before we model it.
sapply(dataset,class)3. head function used to display the first five rows.
head(dataset)4. The class variable is a factor. A factor is a class that has multiple class labels or levels
levels(dataset$Species)5. Class Distribution Let’s now take a look at the number of instances (rows) that belong to each class. We can view this as an absolute count and as a percentage. 6. Summary of each Attribute
summary(dataset)
//Split input and output
x <- dataset[,1:4]
y <- dataset[,5]
Given that the input variables are numeric, we can create box and whisker plots of each.
par(mfrow=c(1,4))
for(i in 1:4) {
boxplot(x[,i], main=names(iris)[i])
}
plot(y)
featurePlot(x=x,y=y,plot=”ellipse”)
featurePlot(x=x,y=y,plot=”box”)
// density plots for each attribute by class value scales <- list(x=list(relation="free"), y=list(relation="free")) featurePlot(x=x, y=y, plot="density", scales=scales)
control <- tarinControl(method=”csv”,number=10)
metric <- “Accuarcy”
set.seed(7) fit.lda <- train(Species~., data=dataset, method="lda", metric=metric, trControl=control) # b) nonlinear algorithms # CART set.seed(7) fit.cart <- train(Species~., data=dataset, method="rpart", metric=metric, trControl=control) # kNN set.seed(7) fit.knn <- train(Species~., data=dataset, method="knn", metric=metric, trControl=control) # c) advanced algorithms # SVM set.seed(7) fit.svm <- train(Species~., data=dataset, method="svmRadial", metric=metric, trControl=control) # Random Forest set.seed(7) fit.rf <- train(Species~., data=dataset, method="rf", metric=metric, trControl=control)We reset the random number seed before reach run to ensure that the evaluation of each algorithm is performed using exactly the same data splits. It ensures the results are directly comparable. Select the best model. We now have 5 models and accuracy estimations for each. We need to compare the models to each other and select the most accurate. We can report on the accuracy of each model by first creating a list of the created models and using the summary function.
# summarize accuracy of models results <- resamples(list(lda=fit.lda, cart=fit.cart, knn=fit.knn, svm=fit.svm, rf=fit.rf)) summary(results)
dotplot(results)
predictions <- predict(fit.lda,validation)
confusionMatrix(predictions,validation$Species)
