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Summary Data Mining classification (1+2) + solutions exercises R58,86
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Summary Data Mining classification (1+2) + solutions exercises

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This document contains a summary of the theory that was completed during this lab session. In addition, at the end of the document, there are solutions for the lab sessions.

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  • August 4, 2023
  • 10
  • 2022/2023
  • Summary
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Classification 1
lag1, lag2,…,lag5: percentage return for each of the five previous trading days

volume: number of shares traded on previous day

today: percentage return on data in question

direction: whether the market was Up or Down on this data

cor(): produces matrix containing all of correlations among the predictors




Here error because “direction” variable is qualitative

Correlations between the lags and today’s returns close to zero => little correlation

Year and volume: substantial correlation

glm(): fits linear models that includes logistic regression (similar to lm() except: family = binomial)

Lag1

 smallest p-value
 negative coefficient: if
market had positive return
yesterday, then less likely to
go up today
 0.15: no clear evidence of
association between Lag1
and direction

, coef(): access coefficients

summary(): access specific aspects of fitted model




predict(): can be used for the probability that the market will go up, given values of predictors

type = “response”: tells R to output probabilities of the form P(Y=1|X)

contrasts(): indicates that R has created a dummy variable




Vector of class predictions based on whether predicted probability of a market increase is greater
than or less than 0.5:




First command: creates vector of 1,250 Down elements

Second command: transforms to Up all of elements for which predicted probability of

market increase exceeds 0.5

table(): produces a confusion matrix



Diagonal elements: correct predictions

Off-diagonal elements: incorrect

Training error rate: 100 – 52.2 = 47.8%

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