December 23, 2020
Bias is the inability of the model to completely identify the general patterns in the training dataset. Variance leads to overfitting i.e. it not only identifies general patters but also the patterns that are inherent to training data(but not test data) and thus decreasing overall test accuracy.
Bias variance trade-off results from the fact that bias is inversely proportional to variance. Hence we make a compromise/trade-off for that values of bias and variance that results in overall minimum errors.
by : Monis Khan
Quick Summary:
Bias is the inability of the model to completely identify the general patterns in the training dataset. Variance leads to overfitting i.e. it not only identifies general patters but also the patterns that are inherent to training data(but not test data) and thus decreasing overall test accuracy. Bias variance trade-off results from the fact that […]