Variance of the Estimator in Machine Learning
Bias and variance describes the sources of error in a supervised learning setting, if your model is underfitting it has high bias, if your model is overfitti...
Bias and variance describes the sources of error in a supervised learning setting, if your model is underfitting it has high bias, if your model is overfitting it has high variance. Its easy to memorise this, but intuition seems a little handwavy, and perhaps we can be a little more concrete on what this means. Variance from where and of what? Note that these terms are with respect to the learned model i.e. the estimator given different samples drawn from the training distribution. What is the bias and variance of the estimator $f$? The expected error where $\mathcal{l}(f, y)$ is the loss…
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