bias-variance-tradeoff
Expanded definition
The bias-variance tradeoff is a fundamental concept in machine learning that describes the tradeoff between two types of errors: bias, which refers to the error due to overly simplistic assumptions in the learning algorithm, and variance, which refers to error due to excessive sensitivity to fluctuations in the training set. A good model strikes a balance between bias and variance, enabling it to generalize well to unseen data while maintaining accuracy.
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bias-variance-tradeoff FAQ
What is bias-variance-tradeoff?
The balance between a model's complexity and its ability to generalize well to new data.
How is bias-variance-tradeoff used in AI systems?
The bias-variance tradeoff is a fundamental concept in machine learning that describes the tradeoff between two types of errors: bias, which refers to the error due to overly simplistic assumptions in the learning algorithm, and variance, which refers to error due to excessive sensitivity to fluctuations in the training set.
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