Bias & Variance in Machine Learning

Machine Learning Fundamentals

Hey there! In this blog, I’ve explained bias and variance. Learn machine learning concepts in simple terms. Happy reading.

Bias denotes how efficiently our machine learning model captures the pattern in training data. Or the amount of error during training the model.

1. If the model has high accuracy in training, then we have a low error which leads to low bias.
2. If the model has low accuracy in training, then we have a high error which leads to high bias.

Variance denotes how efficiently our machine learning model captures the pattern in testing data. Or the amount of error during testing the model.

1. If the model has high accuracy in testing, then we have a low error which leads to low variance.
2. If the model has low accuracy in testing, then we have a high error which leads to high variance.

I believe you have learned something new through my blog. Thanks for reading. Feel free to write suggestions if any improvements needed.