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Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.

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Does multiple cross validations after a model selection make sense?

Let be $M$ a model. Let also be $H_{1 \leq i \leq n}$ some hypothesis of $M$. I have a dataset $\mathcal{D}$ and I want to run $K$-fold cross validation on $\mathcal{D}$ to pick the best model $H_j = …
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