- Invariant rank-3 Cartesian Tensors
- SOT on $B(H)$ different to SOT on $B(H)\otimes 1$
- Problem understanding limit solution
- Eigenvalues - What does it mean that matrix $A$ is “scaling space” by $\lambda_i$ in direction $\mathbf v^{(i)}$
- Intersection between K-topology and lower limite topology
- On ADMM implementation $\|X-B\|_F^2+\lambda\sum_{i=1}^n\|B_i\|_1$
- Number of connected components
- Factorizing an 8×8 unitary matrix into tensor product of three 2×2 unitaries
- Properties matrices's limits
- Applications for finding eigenvalues and eigenvectors of Jacobi operators.
- Bayes rule derivation with vectors
- Evaluate the following vector problems:
- local involution in a riemann surface around a fixed point
- Liner algebra transformations
- Regularization of Improper integral
- Product/Exponential Adjunction in Category of Compactly Generated Hausdorff Spaces
- How to expand taylor series for finding displacement in pixel in an image?
- Property of central product of components
- A uniqueness result for a BVP over a semi-infinite interval
- Endpoint behavior of Legendre Series

# How do you overcome results oriented thinking from a client?

I am working with a client on a special project that I am going to obfuscate in this question. Basically, I'm trying to overcome some short-term, results-oriented thinking from my client.

Let's say you have a model to forecast the performance of a racehorse. Your model tells your client to sell racehorse X because the probability of its performance is low (<10%). The horse is sold and said racehorse goes on to win 3 races. Your client says, "See, we should have never let go of that racehorse! The model is wrong!"

As data scientist we can understand that anomalies happen and that this horse may have also lost some races - we were just on the wrong side of probability this time. But how do you overcome those objections with the client? How do you turn short-term thinking into a long-term outlook of predictive modeling?