Now that we understand what a decision tree is, how it can be used, and what nomenclature is used to describe it, we can wonder about its limitations. This means that the decision tree can be used for both tasks - classification and regression. When the data is classified, this means the tree is performing a classification task, and when the quantity of data is found, the tree is performing a regression task. In the previous examples, observe how the tree could either classify new data as participant or non participant, or the questions could also be changed to - "how many are participants?", "how many are pregnant?", "how many live in a rural area?"- leading us to find the quantity of pregnant participants that live in a rural area. If it depends on the answer of a yes or no question for each node and thus each node has at most two children, when sorted so that the "smaller" nodes are on the left, this classifies decision trees as binary trees. Here, we are focusing on giving a general idea of what is a decision tree. Note: There are several types of trees in Computer Science, such as binary trees, general trees, AVL trees, splay trees, red black trees, b-trees, etc. Besides organizing information, a tree organizes information in a hierarchical manner - the order that the information appears matters and leads to different trees as a result.īelow, is an example of the tree that has been described: From this, we can see that the tree has an inherent hierarchy. Notice, that we could do this in a different order, dividing initially by what area the women live and after by their pregnancy status. How do you populate the nodes of a tree? This is where decision trees come into focus.įirst, we can divide the records by pregnancy, after that, we can divide them by living in urban or rural areas. By using a tree structure, you will be able to represent the different divisions for each category. There is a computational structure that does exactly that, it is the tree structure. The researchers want to understand how many women would be in each category. The group has collected 100 data records and wants to be able to organize those initial records by dividing the women into categories: being or not pregnant, and living in rural or urban areas. In this in-depth hands-on guide, we'll build an intuition on how decision trees work, how ensembling boosts individual classifiers and regressors, what random forests are and build a random forest classifier and regressor using Python and Scikit-Learn, through an end-to-end mini-project, and answer a research question.Ĭonsider that you're currently part of a research group that is analyzing data about women. If you aren't familiar with these - no worries, we'll cover all of these concepts. The Random Forest algorithm is one of the most flexible, powerful and widely-used algorithms for classification and regression, built as an ensemble of Decision Trees.
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