Titlebook: Statistical Learning from a Regression Perspective; Richard A. Berk Textbook 2020Latest edition Springer Nature Switzerland AG 2020 classi

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Endometrium

Dawdle

Statistical Learning as a Regression Problem,ion by some as a form of supervised machine learning. Once these points are made, the chapter turns to several key statistical concepts needed for statistical learning: overfitting, data snooping, loss functions, linear estimators, linear basis expansions, the bias–variance tradeoff, resampling, algorithms versus models, and others.

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Neural Networks,al networks,” or more broadly, “deep learning.” These newer developments have generated both genuine excitement and some self-serving hype. In this chapter, we will begin with early neural networks and end with some of the impressive recent advances.

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漫步

Classification and Regression Trees (CART), also see that although recursive partitioning has too many problems to be an effective, stand-alone data analysis procedure, it is a crucial component of more powerful algorithms discussed in later chapters. It is important, therefore, to get into the details.

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阴险

Boosting,ore poorly on the last pass are given more weight. In that way, the algorithm works more diligently to fit the hard-to-fit observations. In the end, each set of fitted values is combined in an averaging process that serves as a regularizer. Boosting can be a very effective statistical learning procedure.

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