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InterviewSolution
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What is Curse of Dimensionality? |
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Answer» For various distance-based measures like KNN (K-Nearest neighbour) method, the performance or predictive power of the model deteriorates with the increase in numbers of features required for prediction. This is an obvious fact that high- dimensional spaces are vast. Points in high-dimensional spaces tend to be DISPERSING from each other more compared with the points in low-dimensional space. It is apparent that with the increase in dimensions, distance between points increases logarithmically, which gives us the hint that we need to have an exponential increase in data points with the increase in dimensions in order to MAKE machine LEARNING algorithms work correctly. It can be proved that with the increase in dimensions, MEAN distance increases logarithmically. Hence the higher the dimensions, the more data is needed to overcome the curse of dimensionality! |
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