They see injuries for regression and classification have a number of advantages over the classical approaches,
such as linear regression, logistic regression or linear discriminant analysis.
Here are some of the advantages of the Henries.
Foster's trees are very easy to explain to people.
In fact, they are even easier to explain than linear regression.
Secondly, some people believe that this country's more closely mirror human decision making, then
regression or classification approaches based on linear regression or logistic regression.
Thirdly, trees can be displayed graphically and are very easily interpreted even by a non expert.
And lastly, trees can easily handle qualitative predictors without the need to create dummy variables.
As for the disadvantages, that is one major disadvantage of a simple importunity entry.
A simple entry generally does not have the same level of predictive accuracy as some of the other regulation
or classification approaches.
But by aggregating many decision trees.
Or what we call as creating ensembles, we can significantly improve the performance of visionary's.
We'll be looking at on sampling techniques in the coming videos.