How Uber uses machine learning to achieve hyper-growth in sales.

Application

  • User acquisition: how likely a new individual is to purchase a product.
  • Cross-or-up-selling: how propensity of an existing user to purchase a related product.
  • User churn: how likely an existing user is to cancel the purchase.

Problem

Mathematically

  • Parameters are indiscernible from one another.
  • The feature space is sparse— leading to overfitting.
  • Compute needed grows exponentially.
  • Model interpretation & diagnostics are cumbersome.

Pragmatically

Solution: mRMR

  • A matrix of m features: X
  • A mutual information function: I(.)
  • A response class vector: Y
  • Replacing integrals with summations for discrete features.
  • Replacing the quotient with a subtraction where scale-invariant features are used.
  • Replacing the probability density estimates p(x,y), p(y) or p(x) with statistical tests (t-tests &/or F-tests) where severe computational limitations arise.
  • Non-linear feature dependency can be achieved by mapping the data to some linearly separable space with kernel functions.
  • If the downstream classification framework is pre-determined it may be pragmatic to incorporate the related accuracy metric as a proxy for mutual information.

Empirical Findings

Production Implementation

Conclusion

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Statistician, scientist, technologist — writing about stats, data science, math, philosophy, poetry & any other flavours that occupy my mind. Get in touch

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Zach Wolpe

Zach Wolpe

Statistician, scientist, technologist — writing about stats, data science, math, philosophy, poetry & any other flavours that occupy my mind. Get in touch

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