knitr::opts_chunk$set(echo = TRUE) library( liver ) library( pROC ) library( ggplot2 )

The `liver`

package contains a collection of helper functions that make various techniques from data science more user-friendly for non-experts.

Here is an example to show how to use the functinality of the package by using the *churn* dataset which is available in the package.

data( churn ) # load the 'churn' dataset str( churn )

It shows that the 'churn' dataset as a `data.frame`

has `r ncol( churn )`

variables and `r nrow( churn )`

observations.

We partition the *churn* dataset randomly into two groups: train set (80%) and test set (20%). Here, we use the `partition`

function from the *liver* package:

set.seed( 5 ) data_sets = partition( data = churn, prob = c( 0.8, 0.2 ) ) train_set = data_sets $ part1 test_set = data_sets $ part2 actual_test = test_set $ churn

The *churn* dataset has `r ncol( churn ) - 1`

predictors along with the target variable `churn`

. Here we use the following predictors:

`account.length`

, `voice.plan`

, `voice.messages`

, `intl.plan`

, `intl.mins`

, `day.mins`

, `eve.mins`

, `night.mins`

, and `customer.calls`

.

First, based on the above predictors, find the k-nearest neighbor for the test set, based on the training dataset, for the k = 8 as follows

formula = churn ~ account.length + voice.plan + voice.messages + intl.plan + intl.mins + day.mins + eve.mins + night.mins + customer.calls predict_knn = kNN( formula, train = train_set, test = test_set, k = 8 )

To report Confusion Matrix:

conf.mat( predict_knn, actual_test ) conf.mat.plot( predict_knn, actual_test )

To report Mean Squared Error (MSE):

```
mse( predict_knn, actual_test )
```

The predictors that we used in the previous part, do not have the same scale. For example, variable `day.mins`

change between `r min( churn $ day.mins )`

and `r max( churn $ day.mins )`

, whereas variable `voice.plan`

is binary. In this case, the values of variable `day.mins`

will overwhelm the contribution of `voice.plan`

. To avoid this situation we use normalization. So, we use min-max normalization and transfer the predictors as follows:

predict_knn_trans = kNN( formula, train = train_set, test = test_set, k = 8, transform = "minmax" )

To report Confusion Matrix:

conf.mat.plot( predict_knn_trans, actual_test ) conf.mat.plot( predict_knn, actual_test )

To report the ROC curve, we need the probability of our classification prediction. We can have it by using:

prob_knn = kNN( formula, train = train_set, test = test_set, k = 8, type = "prob" )[ , 1 ] prob_knn_trans = kNN( formula, train = train_set, test = test_set, transform = "minmax", k = 8, type = "prob" )[ , 1 ]

To visualize the model performance between the raw data and the transformed data, we could report the ROC curve plot as well as AUC (Area Under the Curve) by using the `plot.roc`

function from the **pROC** package:

roc_knn = roc( actual_test, prob_knn ) roc_knn_trans = roc( actual_test, prob_knn_trans ) ggroc( list( roc_knn, roc_knn_trans ), size = 0.8 ) + theme_minimal() + ggtitle( "ROC plots with AUC") + scale_color_manual( values = c( "red", "blue" ), labels = c( paste( "AUC=", round( auc( roc_knn ), 3 ), "; Raw data; " ), paste( "AUC=", round( auc( roc_knn_trans ), 3 ), "; Transformed data" ) ) ) + theme( legend.title = element_blank() ) + theme( legend.position = c( .7, .3 ), text = element_text( size = 17 ) ) + geom_segment( aes( x = 1, xend = 0, y = 0, yend = 1 ), color = "grey", linetype = "dashed" )

To find out the optimal value of `k`

based on *Error Rate*, for the different values of k from 1 to 30, we run the k-nearest neighbor for the test set and compute the *Error Rate* for these models, by running `kNN.plot()`

command

kNN.plot( formula, train = train_set, test = test_set, transform = "minmax", k.max = 30, set.seed = 3 )

The plot shows that the minimum value of *Error Rate* is for the case that k is 13; the smaller values of *Error Rate* indicates better predictions.

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