Register Now

It will take less than 1 minute to register for lifetime. Bonus Tip - We don't send OTP to your email id Make Sure to use your own email id for free books and giveaways

Cross Validation and varImp in R

I was onto our next book – Linear,Ridge, LAASO, and Elastic Net Algorithm explained in layman terms with code in R , when we thought of covering the simple concepts which are quite helpful while creating models.

Cross Validation is one simple concept which definitely improves the performance of your model. A lot of you must be using this to create a k-fold cross validation

Let’s quickly go through this relatively simple concept and there is no better way than starting with code

`cv <- trainControl(method="repeatedcv",number=10,repeats = 5,verboseIter = T)`

Here we are creating a variable which holds a property i.e. whenever this variable ‘cv’ is called, it will ask the model definition to divide the dataset in 10 equal parts and train the model on 9 parts while testing on the last one i.e. Train on N-1 data points

repeats = 5 means the above process will repeat 5 times i.e. this 9-1 split train and test is done 5 times.

What would you do with this regressive training?
We will compute different Root Mean Square Error, R Square and Mean Absolute Error, and will then decide the best model.

And this is how we use it in a Ridge model

```ridge <- train(medv~.,
BD,
method = 'glmnet',
tuneGrid=expand.grid(alpha=0,lambda=seq(0.0001,1,length=10)),
trControl=cv
)```

So, here we are creating a Ridge Regression model, predicting the value of medv on the dataset BD and the package/function is glmnet, the tuning parameter tells the model that it’s a ridge model(alpha=0) and a total of 10 numbers ranging from 0.0001 and 1 (Equally spaced)

After all this we specify the model to use the cross validation with trControl parameter

The next function which I love while creating models is varImp. This is a simple function which finds out the most important variables in a set of variables. I think it’s a part of the caret package(do check)

varImp(Lasso, scale = F)

Here we have at least 3 and at max 4 important variables to consider in the model. You can also plot the same using the below function

plot(varImp(Lasso,scale=F)

Just a short article covering a couple of concepts.

Keep Learning đź™‚

The Data Monk