Memory-based Algorithms
Memory-based algorithms are a type of machine learning algorithm that relies on the entire training dataset to make predictions. These algorithms store the training data in memory and use it to calculate the similarity between the new data and the training data. The most common memory-based algorithm is k-Nearest Neighbor (k-NN), which predicts the class of a new data point based on the class of its k-nearest neighbors in the training data. Memory-based algorithms are simple and easy to implement, but they can be computationally expensive and suffer from the curse of dimensionality when dealing with high-dimensional data.
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