Instance-based Learning
Instance-based learning is a type of machine learning algorithm that learns by memorizing the training dataset. It makes predictions based on the similarity between the new input instance and the instances in the training dataset. The algorithm stores the entire training dataset and uses it to make predictions at runtime. Instance-based learning is also known as lazy learning because it defers the computation until a new instance needs to be classified. This approach is useful when the relationship between the input and output is complex and difficult to model using traditional algorithms. Instance-based learning is commonly used in classification and regression problems, and it is particularly effective when the training dataset is small or noisy.
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