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312-2012: Handling Missing Data by Maximum
312-2012: Handling Missing Data by Maximum

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... • Let X be a data sample whose class label is unknown • Let H be a hypothesis that X belongs to class C • For classification problems, determine P(H|X): the probability that the hypothesis holds given the observed data sample X • P(H): prior probability of hypothesis H (i.e. the initial probabilit ...
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... * Note this statement provides a general sense of what a confidence interval does for us in concise language for ease of understanding. The specific statistical interpretation is that if many independent samples are taken where the levels of the predictor variable are the same as in the data set, an ...
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... predict. One advantage of train predict is that it can save disk space by not having to write model files. (Typically, predictions take up much less disk space than models.) In particular, train predict can be used for one-vs-all training for multi-class categorization, whereas train test cannot si ...
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Forecasting

Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology, the terms ""forecast"" and ""forecasting"" are sometimes reserved for estimates of values at certain specific future times, while the term ""prediction"" is used for more general estimates, such as the number of times floods will occur over a long period.Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible.
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