Classification is a core supervised learning technique in data mining that assigns predefined labels to data points based on their features. The goal is to predict the category or class of new data points by learning from a labeled training dataset. Classification is widely used for tasks such as ...
• Classification, where a model or
classifier is constructed to predict class
(categorical) Labels
• Training Phase & Testing Phase
• Supervised learning and unsupervised
learning
• The accuracy of a classifier on a given
test set is the percentage of test set
tuples that are correctly classified by the
classifier
Dr.Priya Govindarajan
,Decision Tree Induction
• Decision tree induction is the learning of decision trees from class-labeled training tuples.
• Decision Tree is a Supervised learning technique that can be used for both classification an
problems, but mostly it is preferred for solving Classification problems. It is a tree-structur
where internal nodes represent the features of a dataset, branches represent the decision ru
leaf node represents the outcome.
• A decision tree simply asks a question, and based on the answer (Yes/No), it further split
subtrees.
Dr.Priya Govindarajan
, Example: Suppose there is a candidate who has a job offer and wants to decide whether he should accep
or Not.
Dr.Priya Govindaraj
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