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<h1>Practical Machine Learning Project</h1>
<p><strong>20-Jun-2014</strong></p>
<p><em>Summary</em></p>
<p>Given my unfamiliarity with R, (as well as R markdown) and a limited amount of time, I elected to perform a simple analysis of the datasets provided:</p>
<ul>
<li> Clean up empty columns</li>
<li> Split the training data into regression and cross-validation datasets</li>
<li> Select relevant factors</li>
<li> Preprocess the data using PCA</li>
<li> Fit a RandomForest model to the regression dataset, using internal cross-validation</li>
<li> Determine prediction quality on cross-validation portion of the training dataset</li>
<li> Apply the model to the testing data</li>
</ul>
<p><strong>Clean up dataset</strong></p>
<p>I used the apply function to remove columns with NA's, as well as manually extracted columns that did not appear to be instrument data.</p>
<pre><code class="r">library(AppliedPredictiveModeling)
</code></pre>
<pre><code>## Warning: package 'AppliedPredictiveModeling' was built under R version
## 3.0.3
</code></pre>
<pre><code class="r">library(caret)
</code></pre>
<pre><code>## Warning: package 'caret' was built under R version 3.0.3
</code></pre>
<pre><code>## Loading required package: lattice
## Loading required package: ggplot2
</code></pre>
<pre><code>## Warning: package 'ggplot2' was built under R version 3.0.3
</code></pre>
<pre><code class="r">library(minerva)
tr<-read.csv('pml-training.csv',na.strings=c("NA",""),header=T,stringsAsFactors=F)
te<-read.csv('pml-testing.csv',na.strings=c("NA",""),header=T,stringsAsFactors=F)
# pop out nans
te<-te[!apply(te, 2, function(y) any(is.na(y))) ]
tr<-tr[!apply(tr, 2, function(y) any(is.na(y))) ]
</code></pre>
<p><strong>Split the training data</strong></p>
<p>I used the createDataPartition() function to split the data into training (for regression) and test sets (for manual cross-validation).</p>
<pre><code class="r">inTrain = createDataPartition(tr$classe, p = 0.7)[[1]]
training = (tr[ inTrain,8:59])# select all but classe
testing = (tr[-inTrain,8:59]) # select all but problem id
realtest<-te[,8:59]
</code></pre>
<p><strong>Select relevant factors</strong></p>
<p>Selecting the relevant factors could have been done my simple linear covariance, but I was interested in applying package I've heard about for scoring non-linear correlations, the Minerva library. I used the mine() function to determine the correlated parameters.</p>
<pre><code class="r"># find correlations
#M <- abs(cor(training))
M <- abs(cor(training)) #M<-mine(training) #try to use MINE (takes too long!)
diag(M) <- 0
ix<-which(M > 0.8,arr.ind=T)
print(ix)
</code></pre>
<pre><code>## row col
## yaw_belt 3 1
## total_accel_belt 4 1
## accel_belt_y 9 1
## accel_belt_z 10 1
## accel_belt_x 8 2
## magnet_belt_x 11 2
## roll_belt 1 3
## roll_belt 1 4
## accel_belt_y 9 4
## accel_belt_z 10 4
## pitch_belt 2 8
## magnet_belt_x 11 8
## roll_belt 1 9
## total_accel_belt 4 9
## accel_belt_z 10 9
## roll_belt 1 10
## total_accel_belt 4 10
## accel_belt_y 9 10
## pitch_belt 2 11
## accel_belt_x 8 11
## gyros_arm_y 19 18
## gyros_arm_x 18 19
## magnet_arm_x 24 21
## accel_arm_x 21 24
## magnet_arm_z 26 25
## magnet_arm_y 25 26
## accel_dumbbell_x 34 28
## accel_dumbbell_z 36 29
## gyros_dumbbell_z 33 31
## gyros_forearm_z 46 31
## gyros_dumbbell_x 31 33
## gyros_forearm_z 46 33
## pitch_dumbbell 28 34
## yaw_dumbbell 29 36
## gyros_forearm_z 46 45
## gyros_dumbbell_x 31 46
## gyros_dumbbell_z 33 46
## gyros_forearm_y 45 46
</code></pre>
<p><strong>Preprocess</strong></p>
<p>I used the preprocess() function, as in the quizzes, to perform PCA on the training dataset.</p>
<pre><code class="r">trpp=preProcess(training[,ix],method='pca') # use PCA to preprocess
classe<-tr$classe[inTrain]
trfull<-cbind(predict(trpp,training[,ix]),classe) #set for training and caret crossval
classe<-tr$classe[-inTrain]
tefull<-cbind(predict(trpp,testing[,ix]),classe) # sim testing set
testfull<-predict(trpp,realtest[,ix]) # real testing set
</code></pre>
<p><strong>Fit the Model</strong></p>
<p>I used the RandomForest method (method='rf') to fit the dataset, as in quiz 3. I also used cross-validation as part of the trControl option. This provided another level of protection against overfitting.</p>
<pre><code class="r">modFit1<-train(classe~.,data=trfull,method='rf',trControl=trainControl(method='cv'),number=3,na.action=na.omit)
</code></pre>
<pre><code>## Loading required package: randomForest
</code></pre>
<pre><code>## Warning: package 'randomForest' was built under R version 3.0.3
</code></pre>
<pre><code>## randomForest 4.6-7
## Type rfNews() to see new features/changes/bug fixes.
</code></pre>
<pre><code>## Warning: package 'e1071' was built under R version 3.0.3
</code></pre>
<pre><code>## Loading required package: class
</code></pre>
<pre><code class="r">#modFit2<-train(classe~.,data=training,method='gbm',verbose=F)
save.image("proj_data.RData")
pr<-predict(modFit1,newdata=trfull)
prte<-predict(modFit1,newdata=tefull)
answers<-predict(modFit1,newdata=testfull)
</code></pre>
<p><strong>Determine prediction quality</strong></p>
<p>To determine the quality of the model, my cross-validation set was used with the confusionMatrix() function to examine the confusion matrix to measure the expected out-of-sample-error. The accuracy of the model on the cross-validation set was:</p>
<pre><code class="r">cm<-confusionMatrix(prte,tefull$classe)# look at quality of fit
print(cm)
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1569 60 51 37 13
## B 26 995 44 17 35
## C 42 58 888 68 19
## D 25 9 33 820 25
## E 12 17 10 22 990
##
## Overall Statistics
##
## Accuracy : 0.894
## 95% CI : (0.886, 0.902)
## No Information Rate : 0.284
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.866
## Mcnemar's Test P-Value : 6.36e-06
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.937 0.874 0.865 0.851 0.915
## Specificity 0.962 0.974 0.962 0.981 0.987
## Pos Pred Value 0.907 0.891 0.826 0.899 0.942
## Neg Pred Value 0.975 0.970 0.971 0.971 0.981
## Prevalence 0.284 0.194 0.174 0.164 0.184
## Detection Rate 0.267 0.169 0.151 0.139 0.168
## Detection Prevalence 0.294 0.190 0.183 0.155 0.179
## Balanced Accuracy 0.950 0.924 0.914 0.916 0.951
</code></pre>
<p>Finally, save the output files:</p>
<pre><code>print("write output")
pml_write_files = function(x){
n = length(x)
for(i in 1:n){
filename = paste0("problem_id_",i,".txt")
write.table(x[i],file=filename,quote=FALSE,row.names=FALSE,col.names=FALSE)
}
}
pml_write_files(answers)
</code></pre>
<p><strong>Apply to testing data</strong></p>
<p>The trained random forest model was applied to the blinded testing data, and uploaded to the submission page. The accuracy was close to that of the cross-validation set. 17/20 (85%) were correct</p>
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