Practical Machine Learning Overview

• Prediction study design
• Types of Errors
• Cross validation
• The caret package
• Plotting for prediction
• Preprocessing
• Predicting with regression
• Predicting with trees
• Boosting
• Bagging
• Model blending
• Forecasting

In general, Positive = identified and negative = rejected. Therefore:

• True positive = correctly identified
• False positive = incorrectly identified
• True negative = correctly rejected
• False negative = incorrectly rejected

Medical testing example:

• True positive = Sick people correctly diagnosed as sick
• False positive= Healthy people incorrectly identified as sick
• True negative = Healthy people correctly identified as healthy
• False negative = Sick people incorrectly identified as healthy.

http://en.wikipedia.org/wiki/Sensitivity_and_specificity

library(caret)
library(kernlab)
data(spam)
inTrain <- createDataPartition(y = spam$type, p = 0.75, list = FALSE)
training <- spam[inTrain, ]
testing <- spam[-inTrain, ]

M <- abs(cor(training[, -58]))
diag(M) <- 0
which(M > 0.8, arr.ind = T)

##        row col
## num415  34  32
## direct  40  32
## num857  32  34
## num857  32  40

1. Start with a set of classifiers \(h_1,\ldots,h_k\)
   • Examples: All possible trees, all possible regression models, all possible cutoffs.
2. Create a classifier that combines classification functions: \(f(x) = \rm{sgn}\left(\sum_{t=1}^T \alpha_t h_t(x)\right)\).
   • Goal is to minimize error (on training set)
   • Iterative, select one \(h\) at each step
   • Calculate weights based on errors
   • Upweight missed classifications and select next \(h\)

Adaboost on Wikipedia

http://webee.technion.ac.il/people/rmeir/BoostingTutorial.pdf