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title Practical Machine Learning Overview
author Jeffrey Leek
job Johns Hopkins Bloomberg School of Public Health
logo bloomberg_shield.png
framework io2012
highlighter highlight.js
hitheme tomorrow
url
lib assets
../../libraries
../../assets
widgets
mathjax
mode selfcontained

Practical Machine Learning Content

  • 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

Basic terms

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

Correlated predictors

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

Basic idea behind boosting

  1. Start with a set of classifiers $h_1,\ldots,h_k$
  • Examples: All possible trees, all possible regression models, all possible cutoffs.
  1. 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