What Is Decision Tree Classifier
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What Is Decision Tree Classifier
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Decision Tree Classifier
A decision tree classifier Read more in the User Guide Parameters criterion gini entropy log loss default gini The function to measure the quality of a split Supported criteria are gini for the Gini impurity and log loss and entropy both for the Shannon information gain see Mathematical formulation A decision tree is a tree-like structure that represents a series of decisions and their possible consequences. It is used in machine learning for classification and regression tasks. An example of a decision tree is a flowchart that helps a person decide what to wear based on the weather conditions. Q2.
Classification How Does Decision Tree Classifier Work For Text
What Is Decision Tree ClassifierDecision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation. A decision tree is one of the most powerful tools of supervised learning algorithms used for both classification and regression tasks It builds a flowchart like tree structure where each internal node denotes a test on an attribute each branch represents an outcome of the test and each leaf node terminal node holds a class label
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