Decision tree is one of the widely used machine learning models. It is easier to interpreted and requires little data preparation.

Decision tree consists of nodes, branches and leaves that is grown using training data. Each node represent the feature and threshold that split data into internal nodes or leaves. Each leaf represent final outcome. The depth of tree is defined by the number of levels that does not include root nodes. It is a top down approach that group data with high similarity into same group (homogeneity). Groups are as different as possible with each other (heterogeneity). …

Katie Liao

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