This document discusses bias-variance dichotomy in machine learning. It defines bias as the difference between predicted and actual values, and variance as how much predictions scatter relative to each other. High bias means underfitting data while high variance means overfitting. The goal is to minimize both for the best model. Examples show that low bias and low variance leads to the best accuracy on both training and testing data, while high bias or variance for either leads to worse accuracy on testing data even if training accuracy is high.

1. Dr. Shailesh Kumar Dewangan
Assistant Professor
Department of Production Engineering,
BIT Mesra Ranchi
Bias-Variance Dichotomy
Bias-Variance Dichotomy

2. 2
Bias-Variance and Dichotomy
• Bias is referring to the gap between predicted value of the
data and actual value of the data.
• Variance- This representing, the predicted value how much
scatter, with relation to each other. The amount that the
estimate of the target function will change given different
training data. Trade-off is tension between the error
introduced by the bias and the variance.
• Dichotomy is defined as a sharp division of things or ideas
into two contradictory parts. An example of dichotomy is
grouping mammals by those that live on land and those that
live in water.

3. 3
Different between Bias &Variance
High Bias
Low Bias
High Variance
Low Variance
Bias
Variance
Bias Error of the
Training data
Variance Error of
the Testing data

4. Low and high value of Bias &Variance
x
y
Error
Degree of Polynomial 1
Underfitting
x
y
Error
Degree of Polynomial 2
x
y
Error
Degree of Polynomial 5
Overfitting
Training Data- High accuracy
Testing Data –Accuracy down
High Bias
Low Variances
Low Bias
Low Variances
Low Bias
High Variances

5. Model-1 Model-2 Model-3
Training Data Error
= 1%
Training Data Error
= 25%
Training Data Error
< 10%
Testing Data Error
= 20%
Testing Data Error
= 25%
Testing Data Error
< 10%
Low and high value of
Bias &Variance with Example
Low Bias
High Variances
High Bias
High Variances
Low Bias
Low Variances

6. Thank You