14 votes

What would be a good machine learning model to predict the next vending machine location?

Let's consider the inputs and outputs for a moment. Say your model takes as inputs things like sales and geolocation. The output is if that location is a vending machine or not. Now...is that useful? ...
8 votes

What would be a good machine learning model to predict the next vending machine location?

I would try to create some explanatory variables for each geographic location, such as the income distribution at this location, traffic, amount of pedestrians, and similar features. Then I would use ...
  • 8,249
5 votes
Accepted

Is the magnitude coefficient vector in Ridge regression monotonic in lambda?

Yes it is. Let $x$ be a fixed design matrix and $y$ a fixed response vector. Let $V \Sigma U^{T}$ be the SVD of $x$ and $U \Lambda U^{T}$ be the eigendecomposition of $x^{T} x$, where it is important ...
3 votes

How could stochastic gradient descent save time compared to standard gradient descent?

Not only does SGD iterate gradients much faster, the stochasticity (noise from randomly picking samples) itself can be an asset for generalization: see ex. On the Generalization Benefit of Noise in ...
  • 457
2 votes

What would be a good machine learning model to predict the next vending machine location?

@whuber made a very good point: "Placing a new vending machine 'cannibalizes' sales from nearby ones". Cannibalization may be desirable or not, depending on whether the OP is eating the ...
1 vote

What would be a good machine learning model to predict the next vending machine location?

If you want to frame it as a time series problem, then you might want to go the autoregressive decoder route, where you feed in the sequence of past "outputs" (using teacher forcing, as is ...
1 vote

How to *formalize mathematically* that a binary classifier has no predictive performance?

This is complicated, since supervised learning can have so many flavors, but a few general principles can lead you to solve special cases as they arise. The first important topic to consider is what a ...
  • 35.7k
1 vote

Is the magnitude coefficient vector in Ridge regression monotonic in lambda?

Another way of looking at it is that ridge regression is the Lagrangian for a related constrained optimisation problem, see my answer to a related question Say we are optimizing a model with ...
1 vote

Is the magnitude coefficient vector in Ridge regression monotonic in lambda?

Intuitive viewpoint The regression is a trade-off in the terms $RSS$ and $||w||^2$, which are being balanced. If for some change in lambda the regression solution changes, then the one term increases ...

Only top scored, non community-wiki answers of a minimum length are eligible