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The squared bias decreases monotonically and the variance increases monotonically as a general rule, as we use more flexible methods, the variance will increase and the bias will decrease.Advanced proprietary computer software OriginPro 2019 Crack for an interactive scientific data analysis.
The test MSE intially declines as flexibility increases but at some point it levels off and then starts to increase again (U-shape), this is because when a \(f\) curve yields a small training MSE but a large test MSE we are actually overfitting the data (our procedure tries too hard to find patterns in the training data that are maybe only caused by chance rather than by true properties of the unknown \(f\)). The training MSE declines monotonically as flexibility increases, this is because as flexibility increases the \(f\) curve fits the observed data more closely.
In general, do we expect the performance of a flexible statistical learning method to perform better or worse than an inflexible method when : For each of parts (a) through (d), indicate whether i.