Bias Variance Tradeoff | Bias and Variance in Machine learning | Great Learning

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In simple words, bias means how far you have come in predicting the desired value from your actual value. It is an approach that can ultimately make or break the model in favor or against your idea. A straightforward example can be: When we talk about the famous linear regression model, we quantify the relationship between X and Y variable as linear; on the contrary, in reality, the relationship might not be perfectly linear as we had read. Variance is the reverse of bias. It is called the variance when your model performs exceptionally well on the training dataset yet fails to live up to the same standards when running it on an entirely new dataset. In simple words, your model conveys to you that the predicted values are very scattered from the actual values. This concept is similar to the overfitting of the model on the dataset, also called the difference between the model fits when used on different datasets.

01:25 – Agenda
01:56 – Introduction
04:35 – Bias and Variance in Machine Learning
07:42 – Difference between Bias and Variance
08:15 – Bias vs Variance
13:14 – Bias Variance Trade-Off
18:03 – Bias and Variance In Machine Learning
18:34 – Total Error
21:02 – Hands-On
27:00 – Summary

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santosh chandra says:

Great..bias should be how data set is confirming to your search criterion based on your first impression or assumption of eqvivalent of yes ..

priya kant says:

Brilliant presentation… Would love if a industry example is solved.

TheKlaskyCsupoFan2012 The Pinkfong Fan / Click Yes says:


๐ŸŒŸEgor Ver Films Inc. - Quality Since 2020.๐ŸŒŸ says:

Helpful ๐Ÿ‘๐Ÿ‘๐Ÿ‘๐Ÿ‘๐Ÿ‘

The Last Minute Trade says:

Thanks for sharing Bias concept.
This is very practical nd effective ๐Ÿ˜๐Ÿ‡ฎ๐Ÿ‡ณ

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