bias variance trade off

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  PaulMSimpson Member Posts: 8 Contributor II

  February 2020

  Hi thiru, I think this is a great resource on the bias-variance tradeoff. http://scott.fortmann-roe.com/docs/BiasVariance.html Also, considering that High Variance means you have low Precision, and High Bias means you have low Recall, it may help to use the confusion matrix for binary classification. Note in the images below (one with a high Recall, the next with a high Precision), which have Predicted values as rows and Actual values as columns, that Precision is looking at values going across, and Recall looks at values going down (with TP as the numerator, and TP + FP or TP + FN as the denominator). I think that if your model's results give you a confusion matrix where the numbers let you get a good balance between Precision and Recall, you have a good model that neither overfits nor underfits.
  Regarding the confusion matrix, you can alter it simply by adjusting your threshold (the cutoff point at which you decide whether to call a predicted probability a "Yes" or a "No"). My R Shiny app, Predictive Thresholds, Performance Measures and the Confusion Matrix, as described in this article, allows you to examine the effect on the confusion matrix of adjusting your threshold up or down from the standard 50% threshold. 

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  [Deleted User] Posts: 0 Learner III

  February 2020

  Hello
  
  also this video for Andrew Ng
  
  https://www.youtube.com/watch?v=SjQyLhQIXSM
  
  All the best
  mbs

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