2 votes
Accepted

Why are sklearn's cross_val_score values not increasing with the size of the training set?

I don't think this result is too surprising. Each of the points in your plot has an associated error measurement associated with it. The overall number of holes only varies in a small range, so the ...
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  • 80.3k
2 votes

sklearn's permutation_importance returns surprising result

Permutation importance as implemented by Scikit for a linear model is based on the variance explained $R^2$ which is affected by both the coefficients and the variance of the variable underlying them. ...
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  • 689
2 votes

Scikit-learn QuantileRegressor memory allocation error. No issue with statsmodel QuantReg with the same data

The sklearn QuantileRegressor class uses linear programming to solve the quantile regression problem which is much more computationally expensive than iterative reweighted least squares as used by ...
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  • 51
1 vote

What does it mean having 1 as best k parameter in K-NN?

There is nothing "bad" about $k=1$. It's a hyperparameter to tune, so different values would work for different problems. If you did your hyperparameter tuning correctly, i.e. there are no ...
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  • 117k
1 vote

Why are sklearn's cross_val_score values not increasing with the size of the training set?

To add to @sycorax' answer: If I understand the description of your data correctly, you have features: resistivity, density, ... (how many such physical properties do you have?) And in terms of ...
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1 vote

For K-means clusters, how can I ensure each cluster has a minimum of n numbers

You can use faiss. Its clustering model has options like: min_points_per_centroid/ max_points_per_centroid. It has kmeans, but I ...
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1 vote

Graphical lasso numerical problem (not SPD matrix result)

I also have run into this SPD problem. I was unable to avoid it by rescaling my data because I was interested in conducting simulations in a particular (strange) statistical regime. I then found the ...
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