How can decision trees be used for classification and regression?

Decision trees can be used for both classification and regression tasks. In classification, decision trees partition the feature space into distinct regions, assigning each observation to a specific class. This is achieved by recursively splitting the data based on the values of different features, optimizing for certain criteria such as maximizing information gain or minimizing impurities. On the other hand, decision trees can also be used for regression by predicting a continuous target variable instead of assigning class labels. The tree structure allows for the creation of rules or conditions to predict numeric values based on feature values. In this case, each leaf node represents a predicted value, obtained by considering the average or majority value of the training instances in that region. Decision trees, therefore, offer a versatile and intuitive approach for both classification and regression tasks.
This mind map was published on 20 December 2023 and has been viewed 77 times.

You May Also Like

What is the process of buying a product online?

What are push factors of migration?

What are the main concepts and ideas related to formal program verification?

What are the applications of handwritten digit recognition?

What are the key components of a skills framework?

What steps should be taken to develop a skills framework?

What are the challenges in implementing a skills framework?

How can operations management issues be critically analyzed?

How can operations management knowledge be effectively communicated?

How can innovative and evidence-based solutions be developed for complex issues?

What are the steps to start a micro-greenery business?