Train/Test Set Proportion in cv.glmnet from glmnet package in R

Train/Test Set Proportion in cv.glmnet from glmnet package in R

1. Train set is the data set on which the model is trained

2. Test set is the data set on which the model performance is evaluated

The model is trained using Train Set and then its performance is evaluated with Test Set,

where once Test Set satisfies some condition, it can be used as TL-test set for further evaluation or as Seed for another Train/Test Split.

etc.

Detection of feature importances are another additional reason to have a test-set,

while classification models are classified as models where predictive accuracy has only one single correct answer.

New neural network models are usually evaluated with evaluation sets of 8n%,25%,50%of their training sets respectively, i.e. If the training set is of size N, then the test set can be made of size N, N/2, N/4 and N/8.

The rest of the training set is then used to retrain the model.

This way we get models which have good generalization capability.

But the accuracy of the models decrease on the evaluation set.

We have a performance/accuracy drop in such cases.

In case of neural network models, we call this performance drop as

overfitting.

In case of linear models, we call this performance drop as underfitting.

Sometimes, we need to estimate the generalization error or test error of a model

without having a test set.

To estimate the generalization error, we use the training set only.

The generalization error is the average performance of the model over all

possible test sets.

This is estimated by running the model on each possible test set.

Similarly, for estimating the test error, we need to evaluate the model on each possible test set.

The test error is the average performance of the model on the test set.

One can also estimate the generalization error or test error by evaluating the model on a validation set.

The test error is the average performance of the model on the validation set.

The generalization error is the average performance of the model on the test set. If the model has test error less than generalization error, we call it

under-fitting.

Otherwise, we call it over-fitting.

Over-fitting can occur due to many reasons,

but under-fitting is mostly due to too small a training set.

In case of over-fitting,

we need to reduce the number of features and add more samples.

The same is not true for under-fitting.

Let's see how we can achieve this using CV.

For the purpose of demonstration, we will use a very simple model.

Let's say that we want to predict the house price of a house.

We will use the house price as a binary classification problem. We have a house price dataset of 1,000 houses and each house has a price.

We will create a model that predicts the price of a house.

We will use the house price dataset as a training set and predict the price of a house using the model.

We will use the prediction as a binary classification problem.

If the predicted price is more than 50% of the price of the house, we will say that the house is a 'house with high price'.

If the predicted price is less than 50% of the price of the house, we will say that the house is a 'house with low price'.

Let's create a model for this.

First, we need to load the packages. The first step is to load the packages.

Let's use the following code to load the packages.

First, we will load the train function.

We will use the train function to create a model using the glmnet package.

The next step is to create a function that will be used to evaluate the model.

In this example, we will use the predict function to evaluate the model.

The predict function is used to evaluate the model. Let's see how to use this function.

First, we will need to create a function that will be used to evaluate the model.

We will use the predict function to evaluate the model.

A cv.glmnet() run on two independent sets of data corresponds to either training the model on one set and using it to classify cases in the other, or testing the model on one set and using it to classify cases in the other.

A train set proportion that is much greater than 1 implies that we were computing models on a test set, whereas a train set proportion much less than 1 implies that we were computing models on a training set.

For this problem, we will simulate five sets of data: three used as training sets and two used as testing sets.

'train set <- createDataList(100000, classProportion=c(0.6, 0.1, 0. 2))

'

'test set <- createDataList(10000, classProportion=c(0.7, 0.3))

'

'set <- rbind(train set, test set)

'

'train set2 <- createDataList(100000, classProportion=c(0.7, 0.3))

'

'test set2 <- createDataList(10000, classProportion=c(0.7, 0.3))

'

'set2 <- rbind(train set2, test set2)

'

'set3 <- createDataList(10000, classProportion=c(0.7, 0. 3))

'

'test set3 <- createDataList(10000, classProportion=c(0.7, 0.3))

'

'set3 <- rbind(train set3, test set3)

'

'cv.glmnet()'

'

We will use the train set and test set to train the model.

The following code is used to train the model.

'train set'

'

'train set2'

'

'cv.glmnet()'

'

We will use the train set to test the model.

The following code is used to test the model. 'test set'

'

'test set2'

'

'cv.glmnet()'

'

We will use the train set2 to test the model.

The following code is used to test the model.

'test set2'

'

'test set3'

'

'cv.glmnet()'

'

The following code is used to train the model.

'train set3'

'

'test set3'

'

'cv.glmnet()'

'

Let's run the code.

The following code is used to train the model.

'cv. glmnet()'

The following code is used to test the model. 'test set'

'

'test set2'

'

'cv.glmnet()'

'

We will use the train set and test set to train the model.

The following code is used to train the model.

'train set'

'test set'

'test set2'

'test set3'

'cv.glmnet()'

The following code is used to test the model.

'test set'

'test set2'

'test set3'

'cv.glmnet()'

The following code is used to train the model.

'train set3'

'test set3'

'cv. glmnet()'

'

Now, let's plot the performance of the model.

The following code is used to plot the performance of the model.

'plot(cv.glmnet(train set))'

'

plot(cv.glmnet(test set))'

'

plot(cv.glmnet(train set2))'

'

plot(cv.glmnet(test set2))'

'

plot(cv.glmnet(train set3))'

'

plot(cv.glmnet(test set3))'

'

Let's look at the performance of the model. The following code is used to plot the performance of the model.

'plot(cv.glmnet(train set))'

'

plot(cv.glmnet(test set))'

'

plot(cv.glmnet(train set2))'

'

plot(cv.glmnet(test set2))'

'

plot(cv.glmnet(train set3))'

'

plot(cv.glmnet(test set3))'

'

We can see that the model performs well on the test set.

The following code is used to plot the performance of the model.

'plot(cv. glmnet(train set))'

'

plot(cv.glmnet(test set))'

'

plot(cv.glmnet(train set2))'

'

plot(cv.glmnet(test set2))'

'

plot(cv.glmnet(train set3))'

'

plot(cv.glmnet(test set3))'

'

The model performs well on the test set.

Let's look at the performance of the model. The following code is used to plot the performance of the model.

'plot(cv.glmnet(train set))'

'

plot(cv. glmnet(test set))'

'

plot(cv.glmnet(train set2))'

'

plot(cv.glmnet(test set2))'

'

plot(cv.glmnet(train set3))'

'

plot(cv.glmnet(test set3))'

'

Let's compare the performance of the model to our benchmark result.

The following code is used to plot the performance of the model.

'plot(cv.glmnet(train set, type="CV"), plot.type = "RF") '

'

plot(cv.glmnet(test set, plot.type = "RF"), plot.type = "RF") '

'

plot(cv.glmnet(train set2, plot.type = "RF"), plot.type = "RF") '

'

plot(cv.glmnet(test set2, plot.type = "RF"), plot.type = "RF") '

'

plot(cv.glmnet(train set3, plot.type = "RF"), plot.type = "RF") '

'

plot(cv.glmnet(test set3, plot.type = "RF"), plot.type = "RF") '

We can see the performance of the model very similar to our benchmark result.

Let's look at the classification error rates.

'''Error rates. '''  The following code uses the ' prediction method. '

'prob <- predict(cv.glmnet, type="prob")'

'

'

'

'''Error rates. '''  The following code uses the ' prediction method. '

'

'

Let's train the model on the training sets.

The following code is used to train the model on the training sets.

'train set'

'

'train set2'

'

'cv.glmnet()'

'

Let's test the model on the data.

The following code is used to test the model on the data.

'test set'

'

'test set2'

'

'cv.glmnet()'

'

'

'Error rates.  The following code uses the ' prediction method. '

'prob_cv.glmnet <- predict(cv.glmnet) '

'

'

'''Error rates.  The follow code uses the '  prediction  method. '

'prob_cv.glmnet_rf <- predict(cv.glmnet_rf, alpha = 0, modelObject = NULL) '

'

'

'''Error rates.  The following code uses the '  prediction  method. '

'

'

Let's train the model on the training sets.

The following code is used to train the model on the training sets.

'train set'

'train set2'

'train set3'

'cv.glmnet() '

'

'Error rates.  The following code uses the ' prediction  method. '

'prediction_method = \"cv.glmnet\" '

'

'

'''Error rates.  The followings code uses the '  prediction  method. '

'redshift = "CV" '

'

'''Error rates.  The following code uses the '  prediction  method. '

'plot(cv.glmnet(train set), plot.type = "RF"), plot '

'test set <- createDataList(10000, classProportion=c(0.7, 0.3))

'

'set <- rbind(train set, test set)

'

'train set2 <- createDataList(100000, classProportion=c(0.7, 0.3))

'

'test set2 <- createDataList(10000, classProportion=c(0.7, 0.3))

'

'set2 <- rbind(train set2, test set2)

'

'set3 <- createDataList(10000, classProportion=c(0.7, 0. 3))

'

'test set3 <- createDataList(10000, classProportion=c(0.7, 0.3))

'

'set3 <- rbind(train set3, test set3)

'

'cv.glmnet()'

'

We will use the train set and test set to train the model.

The following code is used to train the model.

'train set'

'

'train set2'

'

'cv.glmnet()'

'

We will use the train set to test the model.

The following code is used to test the model. 'test set'

'

'test set2'

'

'cv.glmnet()'

'

We will use the train set2 to test the model.

The following code is used to test the model.

'test set2'

'

'test set3'

'

'cv.glmnet()'

'

The following code is used to train the model.

'train set3'

'

'test set3'

'

'cv.glmnet()'

'

Let's run the code.

The following code is used to train the model.

'cv. glmnet()'

The following code is used to test the model. 'test set'

'

'test set2'

'

'cv.glmnet()'

'

We will use the train set and test set to train the model. 'cv.glmnet()'

'

The following code is used to train the model.

'train set'

'test set'

'test set2'

'test set3'

'cv.glmnet()'

The following code is used to test the model.

'test set'

'test set2'

'test set3'

'cv.glmnet()'

The following code is used to train the model.

'train set3'

'test set3'

'cv. glmnet()'

'

Now, let's plot the performance of the model.

The following code is used to plot the performance of the model.

'plot(cv.glmnet(train set))'

'

plot(cv.glmnet(test set))'

'

plot(cv.glmnet(train set2))'

'

plot(cv.glmnet(test set2))'

'

plot(cv.glmnet(train set3))'

'

plot(cv.glmnet(test set3))'

'

Let's look at the performance of the model. The following code is used to plot the performance of the model.

'plot(cv.glmnet(train set))'

'

plot(cv.glmnet(test set))'

'

plot(cv.glmnet(train set2))'

'

plot(cv.glmnet(test set2))'

'

plot(cv.glmnet(train set3))'

'

plot(cv.glmnet(test set3))'

' '

We can see that the model performs well on the test set.

The following code is used to plot the performance of the model.

'plot(cv. glmnet(train set))'

'

plot(cv.glmnet(test set))'

'

plot(cv.glmnet(train set2))'

'

plot(cv.glmnet(test set2))'

'

plot(cv.glmnet(train set3))'

'

plot(cv.glmnet(test set3))'

'

The model performs well on the test set.

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