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Bias and variance:

 Lets understand the concept of bias and variance. how we can define bias and variance is a major concern. lets understand with an example we have one datasets with x(independent variable),y(dependent) and y'(predicted) variable.

the difference between y and y' is called bias, so the actual value is 5 and we get 6 in predicted called bias.

bias is the error between the average model and the ground truth model.

the bias is the estimated function of the that tell the capacity of the model to predict the value.

high bias means:

the model is underfitting i.e-high error on both training and test data and model is simple.

variance is the average variability in the model prediction for the given datasets.

high variance means:

model is complex and over fitting model i.e less error on training data and high error on test data. so in this case need to work on noisy input.


upper both picture share the bais and lower is variance. left side has low and right side has high. 
this picture describes the underfitting and overfitting issue.



now increasing bias reduce variance but not always.

error-bias**2+variance and irreducible error.

best model has low variance and bias and need to compromise with bias and variance.

example with data is pending with video.



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