Introduction To Machine Learning In R - Landscape Portal
Introduction to Machine Learning in RSebastian Palmas, Kevin Oluoch2019/11/07IntroductionThis hands-on workshop is meant to introduce you to the basics of machinelearning in R: more specifically, it will show you how to use R to work wellknown machine learning algorithms, including unsupervised (k-means clustering) and supervised methods (such as k-nearest neighbours, SVM, randomforest).This introductory workshop on machine learning with R is aimed at participants who are not experts in machine learning (introductory material will bepresented as part of the course), but have some familiarity with scripting ingeneral and R in particular.We will be using sample datasets available in R and from free onlinesources, just be sure that your internet is working to download some of thedata.ObjectivesThe course aims at providing an accessible introduction to various machinelearning methods and applications in R. The core of the courses focuses onunsupervised and supervised methods.The course contains exercises to provide opportunities to apply learnedcode.At the end of the course, the participants are anticipated to be able to applywhat they have learnt, as well as feel confident enough to explore and applynew methods.The material has an important hands-on component and participantsPre-requisites Participants are expected to be familiar with the R syntax and basic plottingfunctionality. R 3.5.1 or higher. The wine dataset needs to be downloaded from an online repository.Overview of Machine LearningMachine learning is a method of data analysis that automates analytical modelbuilding. It is a branch of artificial intelligence based on the idea that systems
INTRODUCTION TO MACHINE LEARNING IN Rcan learn from data, identify patterns and make decisions with minimal humanintervention.Machine learning algorithms are often categorized as supervised or unsupervised. In supervised learning, the learning algorithm is presented with labelled example inputs, where the labels indicate the desired output. Supervisedalgorithms are composed of classification, where the output is categorical, andregression, where the output is numerical. In unsupervised learning, no labelsare provided, and the learning algorithm focuses solely on detecting structurein unlabelled input data.Note that there are also semi-supervised learning approaches that uselabelled data to inform unsupervised learning on the unlabelled data to identifyand annotate new classes in the dataset (also called novelty detection).PackagesR has multiple packages for machine learning. These are some of the mostpopular: caret: Classification And REgression TrainingrandomForest: specific for random forest algorithmnnet: specific for neural networksRpart: Recursive Partitioning and Regression Treese1071: SVM training and testing modelsgbm: Generalized boosting modelskernlab: also for SVMI will use the caret package in R. caret can do implementation of validation, data partitioning, performance assessment, and prediction. However,caret is mostly using other R packages that have more information aboutthe specific functions underlying the process, and those should be investigated for additional information. Check out the caret home page for moredetail. We will also use randomForest for the Random Forest algorithm andcaretEnsemble for an example of an ensemble method.In addition to caret, it’s a good idea to use your computer’s resourcesas much as possible, or some of these procedures may take a notably longtime, and more so with the more data you have. caret will do this behindthe scenes, but you first need to set things up. Say, for example, you have anquad core processor, meaning your processor has four cores essentially actingas independent CPUs. This is done by allowing parallel processing using thedoSNOW package.The other packages that we will use are: tidyverse: for data manipulation corrplot: for a correlation plot2
INTRODUCTION TO MACHINE LEARNING IN R3If you don’t have them installed, please l.packages("randomForest")To start, let’s load some dyverse)Data Set – WineWe will use the wine data set from the UCI Machine Learning data repository.These are results of a chemical analysis of wines grown in the same region inItaly. The analysis determined the quantities of 13 constituents found in eachof the three types of wines.The goal is to predict wine quality, of which there are 7 values (integers 39). We will turn this into a binary classification task to predict whether a wineis ‘good’ or not, which is arbitrarily chosen as 6 or higher. After getting thehang of things one might redo the analysis as a multiclass problem or eventoy with regression approaches, just note there are very few 3s or 9s so youreally only have 5 values to work with. The original data along with detaileddescription can be found here, but aside from quality it contains predictorssuch as residual sugar, alcohol content, acidity and other characteristics of thewine.The original data is separated into white and red data sets. I have combined them and created additional variables: color and good, indicating scoresgreater than or equal to 6 (denoted as ‘Good’ or ‘Bad’).wine red - earning-databases/wine-quality/winequalitysep ";")wine white - earning-databases/wine-quality/winequalisep ";")wine red - wine red % %mutate(color "red")wine white - wine white % %mutate(color "white")
INTRODUCTION TO MACHINE LEARNING IN Rwine - wine red % %rbind(wine white) % %mutate(white 1*(color "white"),good ifelse(quality 6,"Good", "Bad") % % as.factor())write.csv(wine, file "wine.csv")The following will show some basic numeric information about the tile.acidityMin.: 3.800Min.:0.08001st Qu.: 6.4001st Qu.:0.2300Median : 7.000Median :0.2900Mean: 7.215Mean:0.33973rd Qu.: 7.7003rd l.sugarMin.:0.0000Min.: 0.6001st Qu.:0.25001st Qu.: 1.800Median :0.3100Median : 3.000Mean:0.3186Mean: 5.4433rd Qu.:0.39003rd Qu.: oxideMin.:0.00900Min.: 1.001st Qu.:0.038001st Qu.: 17.00Median :0.04700Median : 29.00Mean:0.05603Mean: 30.533rd Qu.:0.065003rd Qu.: nsityMin.: 6.0Min.:0.98711st Qu.: 77.01st Qu.:0.9923Median :118.0Median :0.9949Mean:115.7Mean:0.99473rd Qu.:156.03rd 20Min.:0.22001st Qu.:3.1101st Qu.:0.4300Median :3.210Median :0.5100Mean:3.219Mean:0.53134
INTRODUCTION TO MACHINE LEARNING IN R##############################################3rd Qu.:3.3203rd Qu.:0.6000Max.:4.010Max.:2.0000alcoholqualityMin.: 8.00Min.:3.0001st Qu.: 9.501st Qu.:5.000Median :10.30Median :6.000Mean:10.49Mean:5.8183rd Qu.:11.303rd Min.:0.0000Class :character1st Qu.:1.0000Mode :characterMedian :1.0000Mean:0.75393rd Qu.:1.0000Max.:1.0000goodBad :2384Good:4113We can visualize the correlations between all variables in the dataset withthe corrplot::corrplot rrplot(cor(wine[, -c(13, 15)]),method "number",tl.cex 0.5) 0.110.3 0.28 0.330.46 0.250.3 0.1 0.08 0.491 0.220.32volatile.acidity 0.22 1 0.38 0.20.38 0.35 0.410.270.260.23 0.04 0.27 0.65citric.acid 0.32 0.381 0.140.040.130.2 0.1 0.330.06 0.010.090.19residual.sugar 0.11 0.20.14 1 0.130.4 0.50.55 0.27 0.19 0.36 0.040.35chlorides 0.30.380.04 0.131 0.2 0.280.360.040.4 0.26 0.2 0.51free.sulfur.dioxide 0.28 0.350.130.4 0.2 1 0.720.03 0.15 0.19 0.180.060.47total.sulfur.dioxide 0.33 0.410.2 0.5 0.280.72 1 0.03 0.24 0.28 0.27 0.040.7density 0.460.270.10.550.360.030.03 1 0.010.26 0.69 0.31 0.39pH 0.250.26 0.33 0.270.04 0.15 0.240.01 1 0.190.120.02 0.33sulphates 0.30.230.06 0.190.4 0.19 0.280.260.19 1 0 0.04 0.49alcohol 0.1 0.04 0.01 0.36 0.26 0.18 0.27 0.690.12 0 1 0.440.03quality 0.08 0.270.09 0.04 0.20.06 0.04 0.310.020.040.44 1 0.12white 0.49 0.650.190.35 0.510.470.7 0.39 0.33 0.490.030.12 11fixed.acidity0.80.60.40.20 0.2 0.4 0.6 0.8 15
INTRODUCTION TO MACHINE LEARNING IN R6Data partitionThe function createDataPartition from the caret package will produceindices to use as the training set. In addition to this, we will normalize thecontinuous variables to the [0,1] range. For the training data set, this will bedone as part of the training process, so that any subsets under considerationare scaled separately, but for the test set we will go ahead and do it nowset.seed(1234) #so that the indices will be the same when re-runtrainIndices - createDataPartition(wine good,p 0.8,list F)wine train - wine[trainIndices, -c(6, 8, 12:14)] #remove quality and color, as well as density and othewine test - wine[!1:nrow(wine) %in% trainIndices, -c(6, 8, 12:14)]Random forestRandom Forests is a learning method for classification and regression. It isbased on generating a large number of decision trees, each constructed usinga different subset of your training set. These subsets are usually selected bysampling at random and with replacement from the original data set. In thecase of classification, the decision trees are then used to identify a classification consensus by selecting the most common output. In the event, it is usedfor regression and it is presented with a new sample, the final prediction ismade by taking the average of the predictions made by each individual decisiontree in the forest.The portion of samples that were left out during the construction of eachdecision tree in the forest are referred to as the Out-Of-Bag (OOB) dataset. Aswe’ll see later, the model will automatically evaluate its own performance byrunning each of the samples in the OOB dataset through the forest.ImplementationThe R package randomForest is used to create random forests.library(randomForest)## Warning: package ’randomForest’ was built## under R version 3.5.3Tune The ForestBy “tune the forest” we mean the process of determining the optimal numberof variables to consider at each split in a decision-tree. Too many prediction
INTRODUCTION TO MACHINE LEARNING IN R7variables and the algorithm will over-fit; too few prediction variables and thealgorithm will under-fit. so first, we use tuneRF function to get the possibleoptimal numbers of prediction variables. The tuneRF function takes twoarguments: the prediction variables and the response variable.This function also returns a plot on how the error varies depending on thenumber of prediction varialbles.tuneRF returns the several numbers of variables randomly sampled ascandidates at each split (mtry). error.](https://en.wikipedia.org/wiki/Out-of-bag error) prediction error.To build the model, we pick the number with the lowest [Out-of-Bag (OOB)0.177OOB Error0.175mtry 3 OOB error 17.31%Searching left .mtry 2OOB error 17.93%-0.03555556 0.05Searching right .mtry 6OOB error 17.91%-0.03444444 0.050.173##############0.179trf - tuneRF(x wine train[,1:9], # Prediction variablesy wine train good) # Response variable236mtry(mintree - trf[which.min(trf[,2]),1])## [1] 3Fit The ModelWe create a model with the randomForest function which takes as arguments: the response variable the prediction variables and the optimal numberof variables to consider at each split (estimated above). We also get the function to rank the prediction variables based on how much influence they have inthe decision-trees’ results.rf model - randomForest(x wine train[,-10], # Prediction variablesy wine train good, # Response variablemtry mintree, # Number of variables in subset at each splitimportance TRUE # Assess importance of predictors.)rf model#### Call:## randomForest(x wine train[, -10], y wine train good, mtry mintree,importance TRUE)
INTRODUCTION TO MACHINE LEARNING IN R##################Type of random forest: classificationNumber of trees: 500No. of variables tried at each split: 3OOB estimate of error rate: 16.62%Confusion matrix:Bad Good class.errorBad 1389 5190.2720126Good 345 29460.1048314We can have a look at each variable’s influence by plotting their importancebased on different indices given by the importance function.0.10Errorplot(rf model, main "")0.200.30We can have a look at the model in detail by plotting it to see a plot ofthe number of trees against OOB error: the error rate as the number of treesincrease.0100200300400500treesFigure 1: Error rates on random forest modelvarImpPlot(rf model, main idity60100140MeanDecreaseAccuracy0 100300MeanDecreaseGini## ValidationWe can check the model fitness against the test datasetpreds rf - predict(rf model, wine test[,-10])confusionMatrix(preds rf, wine test[,10], positive ’Good’)## Confusion Matrix and Statistics####Reference## Prediction Bad Good##Bad 337988
INTRODUCTION TO MACHINE LEARNING IN R############################################Good 139724Accuracy95% CINo Information RateP-Value [Acc NIR]::::0.8174(0.7953, 0.8381)0.6333 2.2e-16Kappa : 0.5996Mcnemar’s Test P-Value : 0.009369SensitivitySpecificityPos Pred ValueNeg Pred ValuePrevalenceDetection RateDetection PrevalenceBalanced 780.66490.7944’Positive’ Class : GoodMore information on Random Forests https://uc-r.github.io/random forests f66adf80ec9k-Nearest Neighbors (k-NN)We will predict if a wine is good or not. We have the data on good, so this is aproblem of supervised classification.Consider the typical distance matrix that is often used for cluster analysisof observations. If we choose something like Euclidean distance as a metric,each point in the matrix gives the value of how far an observation is from someother, given their respective values on a set of variables.k-NN approaches exploit this information for predictive purposes. Let ustake a classification example, and k 5 neighbors. For a given observationxi , find the 5 closest k neighbors in terms of Euclidean distance based on thepredictor variables. The class that is predicted is whatever class the majorityof the neighbors are labeled as. For continuous outcomes we might take themean of those neighbors as the prediction.So how many neighbors would work best? This is an example of a tuning9
INTRODUCTION TO MACHINE LEARNING IN Rparameter, i.e. k, for which we have no knowledge about its value without doingsome initial digging. As such we will select the tuning parameter as part of thevalidation process.ImplementationThe caret package provides several techniques for validation such as k-fold,bootstrap, leave-one-out and others. We will use 10-fold cross validation. Wewill also set up a set of values for k to try out.train is the function used to fit the models. You can check all availablemethods here. This function is used for: * evaluate, using resampling, theeffect of model tuning parameters on performance * choose the “optimal”model across these parameters * estimate model performance from a trainingsetYou can control training parameters such as resampling method and iterations with the cv opts function. In this case we will use a k-fold crossvalidation (cv) with 5 resampling iterations.cv opts trainControl(method "cv", number 10)knn opts data.frame(.k c(seq(3, 11, 2), 25, 51, 101)) #odd to avoid tiesknn model train(good ., data wine train, method "knn",preProcess "range", trControl cv opts,tuneGrid knn opts)knn model####################################k-Nearest Neighbors5199 samples9 predictor2 classes: ’Bad’, ’Good’Pre-processing: re-scaling to [0, 1] (9)Resampling: Cross-Validated (10 fold)Summary of sample sizes: 4679, 4678, 4679, 4679, 4680, 4679, .Resampling results across tuning 050510.45182130.44769910.45612570.446917710
INTRODUCTION TO MACHINE LEARNING IN R##51 0.7468703 0.4326833##101 0.7451462 0.4272734#### Accuracy was used to select the## optimal model using the largest value.## The final value used for the model was k## 11.Additional information reflects the importance of predictors. For mostmethods accessed by caret, the default variable importance metric regardsthe area under the curve or AUC from a ROC curve analysis with regard to eachpredictor, and is model independent. This is then normalized so that the leastimportant is 0 and most important is 100. Another thing one could do wouldrequire more work, as caret doesn’t provide this, but a simple loop could stillautomate the process. For a given predictor x, re-run the model without x, andnote the decrease (or increase for poor variables) in accuracy that results.One can then rank order those results. I did so with this problem and noticethat only alcohol content and volatile acidity were even useful for this model.K-NN is susceptible to irrelevant information (you’re essentially determiningneighbors on variables that don’t matter), and one can see this in that, if onlythose two predictors are retained, test accuracy is the same (actually a slightincrease).dotPlot(varImp(knn al.sugar020406080100Importance## Validation Now lets see how it works on the test setpreds knn predict(knn model, wine test[,-10])confusionMatrix(preds knn, wine test[,10], positive ’Good’)## Confusion Matrix and Statistics11
INTRODUCTION TO MACHINE LEARNING IN R####Reference## Prediction Bad Good##Bad 276 130##Good 200 692####Accuracy : 0.7458##95% CI : (0.7211, 0.7693)##No Information Rate : 0.6333##P-Value [Acc NIR] : 2.2e-16####Kappa : 0.4351#### Mcnemar’s Test P-Value : 0.0001457####Sensitivity : 0.8418##Specificity : 0.5798##Pos Pred Value : 0.7758##Neg Pred Value : 0.6798##Prevalence : 0.6333##Detection Rate : 0.5331##Detection Prevalence : 0.6872##Balanced Accuracy : 0.7108####’Positive’ Class : Good##We get a lot of information here, but to focus on accuracy, we get around75.04%. The lower bound (and p-value) suggests we are statistically predictingbetter than the no information rate (randomly guessing).Neural networksNeural nets have been around for a long while as a general concept in artificialintelligence and even as a machine learning algorithm, and often work quitewell. In some sense they can be thought of as nonlinear regression. Visuallyhowever, we can see them in as layers of inputs and outputs. Weighted combinations of the inputs are created and put through some function (e.g. thesigmoid function) to produce the next layer of inputs. This next layer goesthrough the same process to produce either another layer or to predict theoutput, which is the final layer. All the layers between the input and outputare usually referred to as ‘hidden’ layers. If there were no hidden layers then itbecomes the standard regression problem.One of the issues with neural nets is determining how many hidden layers12
INTRODUCTION TO MACHINE LEARNING IN R13and how many hidden units in a layer. Overly complex neural nets will sufferfrom a variance problem and be less generalizable, particularly if there is lessrelevant information in the training data. Along with the complexity is thenotion of weight decay, however this is the same as the regularization functionwe discussed in a previous section, where a penalty term would be applied to anorm of the weights.Parallel processingIn general, machine learning algorithms are computationally intensive, requiring a lot of computing power. We can use parallel processing to significantlyreduce computing time of some of these algorithms1 . If you are not set up forutilizing multiple processors the following might be relatively slow. You canreplace the method with nnet and shorten the tuneLength to 3 which will befaster without much loss of accuracy. Also, the function we are using has onlyone hidden layer, but the other neural net methods accessible via the caretpackage may allow for more, though the gains in prediction with additionallayers are likely to be modest relative to complexity and computational cost.In addition, if the underlying function has additional arguments, you may passthose on in the train function itself.We will use parallel processing with type SOCK2 .library(doSNOW)cl - makeCluster(3, type "SOCK")registerDoSNOW(makeCluster(3, type "SOCK"))ImplementationIn here I reduce the nummber of maximum iterations maxit to save time.nnet model train(good ., data wine train,method "avNNet",trControl cv opts,preProcess "range",tuneLength 5,trace F,maxit 10)nnet model## Model Averaged Neural Network#### 5199 samples##9 predictor##2 classes: ’Bad’, ’Good’1It is also more efficient to computing NNusing GPU instead of the more general CPUS.2If you are using a Linux/GNU or macOSyou can use the FORK type. In this case, theenvironment is linked in all processors.
INTRODUCTION TO MACHINE LEARNING IN #############################Pre-processing: re-scaling to [0, 1] (9)Resampling: Cross-Validated (10 fold)Summary of sample sizes: 4680, 4679, 4678, 4680, 4679, 4679, .Resampling results across tuning parameters:size1111133333555557777799999decay0e 001e-041e-031e-021e-010e 001e-041e-031e-021e-010e 001e-041e-031e-021e-010e 001e-041e-031e-021e-010e .39723740.38499890.38772380.3867421Tuning parameter ’bag’ was held constantat a value of FALSEAccuracy was used to select theoptimal model using the largest value.The final values used for the modelwere size 1, decay 0.1 and bag FALSE.Once you’ve finished working with your cluster, it’s good to clean up andstop the cluster child processes (quitting R will also stop all of the child processes).14
INTRODUCTION TO MACHINE LEARNING IN RstopCluster(cl)Validationpreds nnet predict(nnet model, wine test[,-10])confusionMatrix(preds nnet, wine test[,10], positive ’Good’)## Confusion Matrix and Statistics####Reference## Prediction Bad Good##Bad 289 163##Good 187 659####Accuracy : 0.7304##95% CI : (0.7053, 0.7543)##No Information Rate : 0.6333##P-Value [Acc NIR] : 7.158e-14####Kappa : 0.4132#### Mcnemar’s Test P-Value : 0.2189####Sensitivity : 0.8017##Specificity : 0.6071##Pos Pred Value : 0.7790##Neg Pred Value : 0.6394##Prevalence : 0.6333##Detection Rate : 0.5077##Detection Prevalence : 0.6518##Balanced Accuracy : 0.7044####’Positive’ Class : Good##More information on NNs l-network-models-r ing-visualizing-neural-network-in-r/ st-neural-networks-using-r/15
INTRODUCTION TO MACHINE LEARNING IN R16Ensemble methodYou can combine the predictions of multiple caret models using the caretEnsemblepackage.library(caretEnsemble)## Warning: package ’caretEnsemble’ was built## under R version 3.5.3Given a list of caret models, the caretStack function can be used tospecify a higher-order model to learn how to best combine the predictions ofsub-models together.Let’s first look at creating 4 sub-models for the ionosphere dataset, specifically: Linear Discriminate Analysis (LDA)Logistic Regression (via Generalized Linear Model or GLM)k-Nearest Neighbors (kNN)Random forest(rf)Below is an example that creates these 4 sub-models. This is a slow process.# Example of Stacking algorithms# create submodelscontrol - trainControl(method "repeatedcv", number 10, repeats 3, savePredictions TRUE, classProbs TRUEalgorithmList - c(’lda’, ’glm’, ’knn’, ’rf’)set.seed(1234)models - caretList(good ., data wine train, trControl control, methodList algorithmList)## Warning in trControlCheck(x trControl,## y target): x savePredictions TRUE is## depreciated. Setting to ’final’ instead.##########Warning in trControlCheck(x trControl, y target): indexes not defined in trControl.Attempting to set them ourselves, so eachmodel in the ensemble will have the sameresampling indexes.results - resamples(models)summary(results)#### Call:
INTRODUCTION TO MACHINE LEARNING IN #######summary.resamples(object results)Models: lda, glm, knn, rfNumber of resamples: 3rd Qu.lda 0.7548077glm 0.7525289knn 0.7081131rf 0.8360577ldaglmknnrf1st 0790.69589940.82753471st 24768460.52925753rd Qu.lda 0.4531663glm 0.4489842knn 0.3492237rf 0.6432326ldaglmknnrfdotplot(results)17
INTRODUCTION TO MACHINE LEARNING IN R0.3 0.4 0.5 0.6 0.7 0.8AccuracyKapparfldaglmknn0.3 0.4 0.5 0.6 0.7 0.8AccuracyKappaConfidence Level: 0.95We can see that the Random Forests creates the most accurate model with anaccuracy of 82.75%.When we combine the predictions of different models using stacking, it isdesirable that the predictions made by the sub-models have low correlation.This would suggest that the models are skillful but in different ways, allowing a new classifier to figure out how to get the best from each model for animproved score.If the predictions for the sub-models were highly corrected ( 0.75) thenthey would be making the same or very similar predictions most of the timereducing the benefit of combining the predictions.# correlation between 519621.000000018
INTRODUCTION TO MACHINE LEARNING IN da0.650.750.750.65Scatter Plot MatrixWe can see the LDA and GLM have high correlation and all other pairs of predictions have generally low correlation. Let’s eliminate the glm method because it has the lowest accuracy.algorithmList - c(’lda’, ’knn’, ’rf’)set.seed(1234)models - caretList(good .,data wine train,trControl control,methodList algorithmList)## Warning in trControlCheck(x trControl,## y target): x savePredictions TRUE is## depreciated. Setting to ’final’ instead.##########Warning in trControlCheck(x trControl, y target): indexes not defined in trControl.Attempting to set them ourselves, so eachmodel in the ensemble will have the sameresampling indexes.results - resamples(models)Let’s combine the predictions of the classifiers using a simple linear model.caretStack finds a a good linear combination of chosen classification models. It can use linear regression, elastic net regression, or greedy optimization.# stack using glmstackControl - trainControl(method "repeatedcv",number 10,#number of resampling iterationsrepeats 3,#the number of complete sets of folds to compute
INTRODUCTION TO MACHINE LEARNING IN RsavePredictions TRUE,classProbs TRUE)set.seed(1234)stack.glm - caretStack(models,method "glm",metric "Accuracy",trControl ###########A glm ensemble of 2 base models: lda, knn, rfEnsemble results:Generalized Linear Model15597 samples3 predictor2 classes: ’Bad’, ’Good’No pre-processingResampling: Cross-Validated (10 fold, repeated 3 times)Summary of sample sizes: 14038, 14038, 14038, 14036, 14038, 14037, .Resampling results:Accuracy0.8280646Kappa0.6237795We can see that we have lifted the accuracy to 75.34% which is a smallimprovement over using SVM alone. This is also an improvement over usingrandom forest alone on the dataset, as observed above.We can also use more sophisticated algorithms to combine predictions inan effort to tease out when best to use the different methods. In this case, wecan use the random forest algorithm to combine the predictions. This methodis slower than using glm.# stack using random forestset.seed(1234)stack.rf - caretStack(models,method "rf",metric "Accuracy",trControl stackControl)## note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 .20
INTRODUCTION TO MACHINE LEARNING IN ##########A rf ensemble of 2 base models: lda, knn, rfEnsemble results:Random Forest15597 samples3 predictor2 classes: ’Bad’, ’Good’No pre-processingResampling: Cross-Validated (10 fold, repeated 3 times)Summary of sample sizes: 14038, 14038, 14038, 14036, 14038, 14037, .Resampling results across tuning 62887980.6258433Accuracy was used to select theoptimal model using the largest value.The final value used for the model wasmtry 2.We can see that this has lifted the accuracy to 96.26% an impressive improvement on SVM alone.k-means clusteri
introduction to machine learning in r 2 can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning algorithms are often categorized as supervised or unsu-pervised. In supervised learning, the learning algorithm is presented with la-belled example inputs, where the labels indicate the desired output.
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