Curse of Dimensionality - EXPLAINED!

[@modakad]

Thanks for the detailed explanation. The first part of the Euclidian max-min distance vs #dimensions was revealing ! One point I am thinking over is even though the max-min distance is shrinking, the ranking of distances will (or might) still hold true, irrespective of #dimensions. If that's the case, the algorithms should not loose any discriminative power in theory. In practice, yes, the strain this might bring on compute requirements can make it impractical and hence the needs to reduce dimensions. Would love to hear your thoughts @CodeEmporium

[@msaw504]

How does the curse of dimensionality affect the interpretation of features (their impacts) in the model? For example, in a linear regression (assuming all the requirements are satisfied) if the number of features are large will their coefficients be useless to understand their impacts? How about their p-values, can they be relied on?

[@superghettoindian01]

Really great summary and video as always!🎉🎉🎉🎉

[@CodeEmporium]

Thank you!

[@mikivanousek1030]

You are good at explaining. Thanks and keep it up :)

[@CodeEmporium]

Thanks so much! I shall :)

[@Rizwankhan2000]

@1:30 Is this diff b/w min and max normalized by number of dimensions?

[@almonddonut1818]

Wonderful explanation, thank you!

[@CppExpedition]

wonderful Explanation! i love it!

[@harshitsati]

The thumbnail 😳🤯

[@CodeEmporium]

Thank you :)

[@est9949]

This doesn't sound right.

[@CodeEmporium]

Nothing ever does :)