Titlebook: Assessing and Improving Prediction and Classification; Theory and Algorithm Timothy Masters Book 2018 Timothy Masters 2018 prediction.class

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https://doi.org/10.1007/978-3-319-39253-0od that the distinction is not always clear. In particular, almost no models can be considered to be pure classifiers. Most classification models make a numeric prediction (of a scalar or a vector) and then use this numeric prediction to define a classification decision. Thus, the real distinction i

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https://doi.org/10.1007/978-0-387-33957-3s be estimated. This included model parameters as well as performance measures based on independent test data. Then in Chapter 4 we saw that performance measures for a model could be safely obtained from the very same data that was used to train the model. In this chapter we will explore assorted me

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Tsvetko Prokopov,Stoyan Tanchevsions on numeric predictions, the methods of that chapter are often a good choice. However, some models are inherently strict classifiers in that they produce a class decision and nothing more. Also, many number-based classifiers produce numeric predictions that are unstable in some way. In such sit

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Preventive Measures for Food Safetyl in some way. But even the most sophisticated model is helpless if it is not given the information it needs to make a good decision. In this chapter, we explore the concept of information content of a variable, and we present a variety of algorithms for assessing the amount and nature of this infor

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https://doi.org/10.1007/978-1-4842-3336-8prediction; classification; assess; improve; AI; artificial; intelligence; big data; analytics; statistics; an

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Information and Entropy,l in some way. But even the most sophisticated model is helpless if it is not given the information it needs to make a good decision. In this chapter, we explore the concept of information content of a variable, and we present a variety of algorithms for assessing the amount and nature of this information.