Overfitting/underfitting
We can see that linear function is not sufficient to fit the training samples. This is called underfitting.
A polynomial of degree 2 approximates the true function almost perfectly.
However, for higher degrees of polynomial, the model will overfit the training data.
We evaluate quantitively overfitting/underfitting by using cross-validation. we calculate the error on the test set and compare it with error on training set to determine overfitting or underfitting.
High Bias versus High Variance
High Bias - Both training and test errors are high and both errors are more or less the same.
High Variance - Training error is low but testing is very high compared to training error.
We can see that linear function is not sufficient to fit the training samples. This is called underfitting.
A polynomial of degree 2 approximates the true function almost perfectly.
However, for higher degrees of polynomial, the model will overfit the training data.
We evaluate quantitively overfitting/underfitting by using cross-validation. we calculate the error on the test set and compare it with error on training set to determine overfitting or underfitting.
High Bias versus High Variance
High Bias - Both training and test errors are high and both errors are more or less the same.
High Variance - Training error is low but testing is very high compared to training error.
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