Mahalanobis Distance: Simple Definition, Examples

Mahalanobis distance - SlideServe

SOLVED:Let 𝐒 be the covariance matrix of the data. The Mahalanobis distance between 𝐱^a and 𝐱^b is defined as (𝐱^a-𝐱^b)^⊤ 𝐒^-1(𝐱^a-𝐱^b) Explain how to approximate this distance using M-dimensional PCA.

Insightforge | AI & Data Science on Instagram: "One dot can flip your entire model. Outliers aren’t just “weird points” they’re silent disruptors that can hijack your slope, shift your mean, and crash your accuracy. Let’s break down how to Detect - Decide - Defend your data. Leverage vs Influence: Not every far-off point is dangerous. Check Cook’s Distance (D > 4/n) to find influencers that twist your line of best fit. Robust z with MAD: Go beyond mean-based z-scores. Use median + MAD for stable