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Summary An Introduction to Statistical Learning, ISBN: 9781461471370 Big Data Analysis (7204MM17XY) $8.41
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Summary An Introduction to Statistical Learning, ISBN: 9781461471370 Big Data Analysis (7204MM17XY)
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  • Big Data Analysis (7204MM17XY)
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  • Universiteit Van Amsterdam (UvA)
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  • An Introduction to Statistical Learning

Summary of An Introduction to Statistical Learning - ISBN: 9781461471370 Big Data Analysis (7204MM17XY), Chapters 2-10. Excluding the algorithms, including function descriptions and visualisations for clarity.

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  • Yes
  • December 1, 2021
  • 14
  • 2020/2021
  • Summary

Subjects

  • statistics
  • algorithms
  • data science
  • rstudio
  • statistics
  • regression
  • classification
  • unsupervised learning
  • resampling

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  • Universiteit van Amsterdam (UvA)
  • Master Behavioural Data Science
  • Big Data Analysis (7204MM17XY)
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Chapter 2
2 reasons to estimate F:

- Prediction
- Inference

Parametric methods

- Easy to estimate parameters in a linear function
- Model will usually not match the true unknown form of F

Non-parametric methods

- Avoids (wrong) assumption of functional form of F
- Large number of observations is required in order to obtain an accurate estimate of F

, Variance refers to the amount by which ˆf would change if we estimated it using a different training
data set

Bias refers to the error that is introduced by approximating a real-life problem, which may be
extremely complicated, by a much simpler model.

KNN neighbours
When K = 1, the decision boundary is overly flexible and finds patterns in the data that don’t
correspond to the Bayes decision boundary. This corresponds to a classifier that has low bias but very
high variance.



lowest possible test error rate, called the Bayes error rate.


Chapter 3




Curse of dimensionality: As the number of features/dimensions grows, the amount of data we need
to generalize accurately grows exponentially

Chapter 4
LDA to classify more than 2 classes


Why do we need another method, when we have logistic regression?
There are several reasons:

- When the classes are well-separated, the parameter estimates for the logistic regression
model are surprisingly unstable. Linear discriminant analysis does not suffer from this
problem.
- If n is small and the distribution of the predictors X is approximately normal in each of the
classes, the linear discriminant model is again more stable than the logistic regression model.


Check video’s LDA/QDA

sensitivity is the percentage of true defaulters that are identified
specificity is the percentage of non-defaulters that are correctly identified

LDA is a much less flexible classifier than QDA, and so has substantially lower variance.
LDA tends to be a better bet than QDA if there are relatively few training observations and so
reducing variance is crucial.

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