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They see injuries for regression and classification have a number of advantages over the classical approaches,

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such as linear regression, logistic regression or linear discriminant analysis.

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Here are some of the advantages of the Henries.

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Foster's trees are very easy to explain to people.

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In fact, they are even easier to explain than linear regression.

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Secondly, some people believe that this country's more closely mirror human decision making, then

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regression or classification approaches based on linear regression or logistic regression.

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Thirdly, trees can be displayed graphically and are very easily interpreted even by a non expert.

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And lastly, trees can easily handle qualitative predictors without the need to create dummy variables.

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As for the disadvantages, that is one major disadvantage of a simple importunity entry.

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A simple entry generally does not have the same level of predictive accuracy as some of the other regulation

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or classification approaches.

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But by aggregating many decision trees.

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Or what we call as creating ensembles, we can significantly improve the performance of visionary's.

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We'll be looking at on sampling techniques in the coming videos.