## Error analysis in supervised machine learning

Every supervised learning problem encounters either bias or variance error. Please refer to this page if you want to get more intuition about bias and variance error as it will help in understanding this post. Once you know where(bias or variance) your model is doing wrong, it becomes easier to get the next direction. This…

## What is Bayes Error ? What is the best approximation to bayes error ?

Bayes error is the lowest possible error one can achieve on a set of data samples. Suppose there are 10000 images of an object like chair and the machine learning task is to detect those objects. We find out that the best achievable accuracy is 99.25% by anyone in the world. Bayes error in this…

## Whether to reduce bias error or variance error ?

Supervised learning model’s error can be decomposed in form of bias error and variance error. Read here to get the best intuition about bias and variance error.  When a model doesn’t perform well either on training data or testing data, either of bias error or variance error or both error might be the issue. Therefore…

## How does bias and variance error gets introduced ?

Any supervised learning model is the result of optimizing the errors due to model complexity and the training error(prediction error on examples during training). Example: Ridge Regression or Regularized Linear Regression cost function(with parameters ) is given by     is the mean squared error of the prediction made(by the model with parameters ) on training…

## What is the bias variance trade-off ?

Bias-variance tradeoff is the tradeoff between training error and the test error. In other words, making the training error the lowest possible, may lead to high generalization error on the test set.The bias-variance tradeoff is a core concept in supervised learning. We want to design models that best fit the training data capturing all the subtleties…

## Can you give an example of a classifier with high bias and high variance?

High bias means the data is being  underfit. The decision boundary is not usually complex enough. High variance happens due to over fitting, the decision boundary is more complex than what it should be.   High bias high variance happens when you fit a complex decision boundary that is also not fitting the training set…