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Decision theory

**Predictive modelling** is the process by which a model is created or chosen to try to best predict the probability of an outcome. In many cases the model is chosen on the basis of detection theory to try to guess the probability of a signal given a set amount of input data, for example given an email determining how likely that it is spam.

Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set, say spam or 'ham'.

## Contents

[show]## Models and classifiersEdit

Many models exist to try to predict on the basis of input data.

### Classification trees Edit

### Naive Bayes Edit

*See main article: Naive Bayes classifier*

*k*-nearest neighbor algorithmEdit

*See main article: k-nearest neighbor algorithm*.

### Majority classifierEdit

### Support vector machinesEdit

*See main article: Support vector machine*

### Logistic regressionEdit

Logistic regression is a technique in which unknown values of a discrete variable are predicted based on known values of one or more continuous and/or discrete variables. Logistic regresion differs from OLS regression in that the dependent variable is binary in nature. This procedure has many applications. In biostatistics, the researcher may be interested in trying to model the probability of a patient being diagnosed with a certain type of cancer based on knowing, say, the incidence of that cancer in his or her family. In business, the marketer may be interested in modeling the probability of an individual purchasing a product based on the price of that product. Both of these are examples of a simple, binary logistic model. The model is "simple" in that each has only one independent, or predictor, variable, and it is "binary" in that the dependent variable can take on only one of two values: cancer or no cancer, and purchase or does not purchase. Models are not restricted to a single independent variable or to a binary dependent variable.

## See alsoEdit

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