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4 votes

How to fit sklearn.svm.SVC with three features, given that the features are actually arrays of lengths 128, 12 and 40?

I think there are a few ways one could approach this. The best course of action depends on the nature of the data and task, as well as experimentation. If you could speak more to what the features ...
MuhammedYunus's user avatar
4 votes

How to fit sklearn.svm.SVC with three features, given that the features are actually arrays of lengths 128, 12 and 40?

The direct way is to treat each element as a single feature. Doing so will give you 128+12+40 = 180 features. Depending on your current data-structure ...
Broele's user avatar
  • 1,515
3 votes
Accepted

Why do Shapley value solutions remain consistent when the value function of the empty set changes in the ML context?

At a high level, this mostly goes away by just defining the valuation function $v$ as the expected model output given the coalition (*abusing terminology and notation a little) minus the global ...
Ben Reiniger's user avatar
  • 11.9k
2 votes

The best algorithm(s) for finding the best hyperparameters (special case)

It seems as if you have tried the main tabular machine learning model types but I would suggest you to look into Optuna. It's a model-agnostic hyperparameter optimization framework which is awesome ...
Guest's user avatar
  • 21
1 vote
Accepted

Outputting handwritten digits with a Neural Network

You can use some generative model like GAN for this task. It works this way that there are 2 models: generator and discriminator. The discriminator learns discerning true images (from the dataset) and ...
Tomasz Witkowski's user avatar

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