Decision Tree-Based algorithms for Machine Learning
Updated on: April 12, 2020 · 2 mins read
Decision trees are the type of supervised machine learning algorithm where decision-making is done using trees. The data is split between the states according to the given parameter. The leaf node decides the final decision for the given branch of the tree.
Yesor it can be
No. These trees are known as Classification Trees as you are trying to classify the input data into one of the categories. We also have
Regression Treeswhere the output is not binary. For example, a model to detect the salary of a soccer player using the data present for many soccer players. We are going to discuss a lot about the decision trees in the upcoming days and this is the page that will be updated whenever a new post is available.
Regression TreesAs already discussed, regression tree based models gives the output (leaf nodes) as a real value. For example a model which try to predict the salary of baseball players according to the data like, years of experience and number of hits that the hitter made in the last season. Read more about Regression Trees in the post below.
Classification TreesClassification Trees are the trees in which we classify the values as the output of the model. For example, predicting whether you want to give loan to the incoming customer or not.
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