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About What Is Bias Variance Decomposition - BbGoogle Search
The bias-variance decomposition is a way to analyze the expected generalization error of a learning algorithm as the sum of three terms: bias, variance, and irreducible error. Bias measures how far the learning algorithm's predictions are from the true underlying target function on average. Variance measures how much the algorithm's predictions would change with different training data. The tradeoff is between algorithms with high bias and low variance versus low bias and high variance. The decomposition helps understand how changes to the learning algorithm affect these sources of error.
Detailed Information
| Author: | ['Harshaldhepe'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 3 |
| Language: | English |
| Format: | |
| Price: | FREE |
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