Machine Learning.
Logistic regression is a standard approach to building a predictive model. However, decision trees are an alternative which are clearer and often superior.
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There are two types of predictors in predictive models: numeric and categorical. There are several methods of transforming categorical variables.
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When building a predictive model, it is often practical to improve predictive performance by modifying the numeric variables. This is called transformation.
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A random forest is a collection of decision trees, which is used to learn patterns in data and make predictions based on those patterns.
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Decision trees work by repeatedly splitting the data to lead to the option which causes the greatest improvement. We explain how these splits are chosen.
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A random forest is an ensemble of decision trees. Like other machine-learning techniques, they use training data to learn to make predictions. Read more.
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Machine learning is an issue of trade-offs. Here we look at pruning and other ways of managing these trade-offs in the context of decision trees. Read more.
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