"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

October 10, 2016

Day #35 - Bias Vs Variance


These are frequently occurring terms with respect to performance of model against training and testing data sets.

Classification error = Bias + Variance

Bias (Under-fitting)
  • Bias is high if the concept class cannot model the true data  distribution well, and does not depend on training set size.
  • High Bias will lead to under-fitting
How to identify High Bias
  • Training Error will be high
  • Cross Validation error also will be high (Both will be nearly the same)
Variance(Over-fitting)
  • High Variance will lead to over-fitting
How to identify High Variance
  • Training Error will be high
  • Cross Validation error also will be Very Very High compared to training error
Hot to Fix ?
Variance decreases with more training data, and increases with more complicated classifiers

Happy Learning!!!
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