Overfitting and Underfitting in Machine Learning | Understanding Bias and Variance

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What is overfitting and underfitting in machine learning? What is Bias and Variance?

Overfitting and Underfitting are two common problems in machine learning and Deep learning.

If a model has low training and the test accuracy, we suffer a problem of underfitting and it is called high bias and variance.

If a model has high train accuracy, but low test accuracy, we suffer a problem of overfitting and it is called low bias and high variance.

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Timestamps :
0:00 The Problem
0:50 Overfitting and Underfitting
2:32 Bias and Variance in Deep Learning
3:18 How to solve Underfitting in Machine Learning
4:59 How to solve Overfitting in Machine Learning
6:15 Regularization in Machine Learning

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This is Your Lane to Machine Learning ⭐

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If you found this video helpful, then hit the *_like_* button👍, and don't forget to *_subscribe_* ▶ to my channel as I upload a new Machine Learning Tutorial every week.

CodingLane

At 2:38, isn't there a high bias and low variance during underfitting?

digyaacharya

underfitting has High bias and low variance right @2:45

satviknaren

Nice explanation...but I have a question why decision trees suffer over fitting problem

albert

i get the term "training data set" but what exactly is "test data set" ?

rahulsharma-hmtv

***underfitting should be high bias low variance I guess

itz_me_imraan

Good content, but that accent gives us ptsd from too much tech indians

sELFhATINGiNDIAN

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sanketvarma

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