Understanding Dimensionality Reduction

Author(s): Vikas K Solegaonkar (ThinkPro Systems)

Originally published on Towards AI.

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We all understand that more data means better AI. That sounds great! But, with the recent blast of information, we often end in a problem of too much data! We need all that data. But it turns out to be too much for our processing. Hence we need to look into ways of streamlining the available data so that it can be compressed without losing value. Dimensionality reduction is an important technique that achieves this end.

Consider the simple case of predicting medical expenses based on several parameters. The data may include different parameters related to all the negative habits… Read the full blog for free on Medium.

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Published via Towards AI