Caret R Package - classification and regression training

Caret R Package - classification and regression training
(http://topepo.github.io/caret/index.html)

The caret package (short for classification and regression training) contains functions to streamline the model training process for complex regression and classification problems. The caret package is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for:

• data splitting
• pre-processing
• feature selection
• model tuning using resampling
• variable importance estimation

Following are the steps to install caret package (it has many dependencies).

Install "‘minqa’, ‘RcppEigen’, ‘scales’, ‘lme4’, ‘ggplot2’, ‘reshape2’, ‘BradleyTerry2’" one by one.

Step 1: install.packages (“minqa”)

Step 2: install.packages (“RcppEigen”)

Step 3: install.packages(“lme4”)

Step 4: install.packages(“ggplot2”)

Step 5: install.packages(“reshape2”)

Step 6: install.packages(“BradleyTerry2”)

Step 7:  install.packages("caret", dependencies = c("Depends", "Suggests"))

Example of predicting using “glm” method:

library(caret)

library(kernlab)

data(spam)

inTrain <- createDataParition(y=spam$type,p=0.75,list=FALSE)   #partition 75% training and 25% testing

training <- spam[inTrain, ]

testing <- spam[-inTrain, ]

> dim(training) [1] 3451   58 > dim(testing) [1] 1150   58

>

> set.seed(1234) > fit<-train(type~., data=training, method="glm") Loading required namespace: e1071 There were 26 warnings (use warnings() to see them) > fit Generalized Linear Model 3451 samples   57 predictor    2 classes: 'nonspam', 'spam' No pre-processing Resampling: Bootstrapped (25 reps) Summary of sample sizes: 3451, 3451, 3451, 3451, 3451, 3451, ... Resampling results   Accuracy   Kappa      Accuracy SD  Kappa SD    0.9207482  0.8330317  0.008444636  0.01755059

>

> fit$finalModel

Call:  NULL

Coefficients:

      (Intercept)               make  

       -1.515e+00         -3.393e-01  

          address                all  

       -1.482e-01          9.183e-02  

            num3d                our  

        2.531e+00          5.661e-01  

             over             remove  

        4.999e-01          2.612e+00  

         internet              order  

        5.661e-01          8.957e-01  

             mail            receive  

        9.189e-02         -2.957e-01  

             will             people  

       -1.321e-01         -2.583e-01  

           report          addresses  

        1.068e-01          1.121e+00  

             free           business  

        9.468e-01          1.080e+00  

            email                you  

        1.910e-02          8.164e-02  

           credit               your  

        1.387e+00          2.326e-01  

             font             num000  

        3.465e-01          3.525e+00  

            money                 hp  

        1.376e+00         -1.982e+00  

              hpl             george  

       -1.369e+00         -9.258e+00  

           num650                lab  

        9.965e-01         -2.143e+00  

             labs             telnet  

       -6.141e-01         -1.234e-01  

           num857               data  

        2.369e+00         -9.245e-01  

           num415              num85  

        1.111e+00         -2.231e+00  

       technology            num1999  

        7.566e-01          8.572e-02  

            parts                 pm  

       -5.501e-01         -1.005e+00  

           direct                 cs  

       -2.563e-01         -4.692e+01  

          meeting           original  

       -2.173e+00         -9.787e-01  

          project                 re  

       -1.610e+00         -7.536e-01  

              edu              table  

       -1.483e+00         -3.167e+00  

       conference      charSemicolon  

       -4.491e+00         -1.623e+00  

 charRoundbracket  charSquarebracket  

        1.356e-01         -6.342e-01  

  charExclamation         charDollar  

        2.497e-01          5.745e+00  

         charHash         capitalAve  

        2.223e+00         -1.661e-03  

      capitalLong       capitalTotal  

        8.800e-03          7.263e-04  

Degrees of Freedom: 3450 Total (i.e. Null);  3393 Residual

Null Deviance:     4628 

Residual Deviance: 1297        AIC: 1413

PREDICTIONS:

> predictions<- predict(fit, newdata=testing)

> predictions

   [1] spam    spam    spam    spam    spam   

   [6] spam    nonspam spam    spam    spam   

  [11] spam    spam    spam    spam    spam   

  [16] spam    nonspam spam    spam    spam   

  [21] spam    spam    spam    spam    spam   

  [26] nonspam spam    spam    spam    spam   

  [31] nonspam spam    spam    spam    spam   

  [36] spam    spam    spam    spam    spam   

  [41] spam    spam    spam    spam    spam   

  [46] spam    spam    spam    spam    spam   

  [51] spam    spam    spam    nonspam spam   

  [56] spam    spam    spam    spam    spam   

  [61] spam    spam    spam    spam    spam   

  [66] spam    spam    spam    spam    spam   

  [71] spam    spam    spam    spam    nonspam

> confusionMatrix(predictions,testing$type) Confusion Matrix and Statistics           Reference Prediction nonspam spam    nonspam     659   50    spam         38  403                                                          Accuracy : 0.9235                           95% CI : (0.9066, 0.9382)     No Information Rate : 0.6061               P-Value [Acc > NIR] : <2e-16                                                                      Kappa : 0.839            Mcnemar's Test P-Value : 0.241                                                                 Sensitivity : 0.9455                       Specificity : 0.8896                   Pos Pred Value : 0.9295                   Neg Pred Value : 0.9138                       Prevalence : 0.6061                   Detection Rate : 0.5730             Detection Prevalence : 0.6165                 Balanced Accuracy : 0.9176                                                           'Positive' Class : nonspam

>