A decision tree algorithm works by splitting the data into branches based on feature values, which helps to make a decision or prediction. Each internal node represents a decision point…
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#dataanalysis
Can you explain how a decision tree algorithm works in the context of AI agents and agentic workflows?
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Can you explain the difference between supervised and unsupervised learning and provide examples of when to use each type?
Supervised learning uses labeled data to train models, making predictions based on input-output pairs, while unsupervised learning uses unlabeled data to identify patterns or groupings. You would use supervised learning…
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