Table of Contents
What is the relationship between bias and variance?
Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is 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.
Is there any relation between regularization and the VC dimension?
Basically it says that with regularization (augmented error) the VC dimension does not change, so it proposes to use of the effective number of parameters as a good surrogate for the VC dimension.
Are bias and variance inversely proportional?
In general, if you increase the complexity of the underlying system, the bias of the system decreases while the variance increases. They both are inversely proportional to each other.
What is the relationship between bias and data?
Also called “error due to squared bias,” bias is the amount that a model’s prediction differs from the target value, compared to the training data. Bias error results from simplifying the assumptions used in a model so the target functions are easier to approximate. Bias can be introduced by model selection.
What is PAC theory?
Summary. Probably approximately correct (PAC) learning is a theoretical framework for analyzing the generalization error of a learning algorithm in terms of its error on a training set and some measure of complexity. The goal is typically to show that an algorithm achieves low generalization error with high probability …
Can VC dimension be infinite?
The VC-dimension of the set of classifiers that output the sign of a sine wave parametrized by a single parameter (the angular frequency of the sine wave) is infinite.
What is the formula for bias?
bias(ˆθ) = Eθ(ˆθ) − θ. An estimator T(X) is unbiased for θ if EθT(X) = θ for all θ, otherwise it is biased.
How do neural networks reduce variance and bias?
How to lower the variance?
- Increase the training set data.
- Try Regularisation.
- Try a different Neural Network Architecture.