I have two models trained on a number of tags and use it for predicting the genre of a game. I noticed that due to have the models were trained, sometimes the same input data can have the two models output wildly different genres.

I am wondering the following about how it works internally:
when combined with 5-fold cross validation for example, does TunedCalibratedCV take the four training folds then performs 5-fold cross-validation for those 4 folds to determine the optimal threshold and then the model is evaluated on the 5th original fold?

I am wondering the following about how it works internally:
when combined with 5-fold cross validation for example, does TunedCalibratedCV take the four training folds then performs 5-fold cross-validation for those 4 folds to determine the optimal threshold and then the model is evaluated on the 5th original fold?

I have a set of code that when I run in a python interpreter (3.8.4) everything works fine. However, when I try to run via command line, I end up receiving an error. the error in full is

I have a stratified labels for categorizing games. Ideally I planned to use a final level to train my model and then fill back the earlier levels.
a brief example of the setup

I have a stratified labels for categorizing games. Ideally I planned to use a final level to train my model and then fill back the earlier levels.
a brief example of the setup

example contrived for this question. say, I am training a binary classifier utilizing sklearn package. i have a balanced dataset , half positive and half negative samples. i split the train/test data ( sample code below). I want to train this, such that i have good enough precision and recall values. I definitely want to have more false positive than false negatitve. I understand i can adjust cutoff value , and that cutoff value can impact precision and accuracy of my model. is it a good strategy to adjust cutoff during training , until you get desired precision recall value?

I have a regression model made using random-Forest. I made pipelines using scikit to process data and now have used RandomForestRegressor to predict.
I want to get the accuracy of model. because of the problem of over-fitting I decided to use the cross_val_score function to get rid of that.

I would expect that when considering a large sample from a bivariate Gaussian population, covariance estimates from sklearn.covariance.MinCovDet should be equivalent to those from numpy.cov ? Yet when I test this using the following code, I get systematically smaller variance estimates using MinCovDet. Why is that?

Is it possible to use FunctionTransformer to emulate OneHotEncoder? More generally, is it possible to write a FunctionTransformer such that the number of columns in the output DataFrame is different (either lower or higher) than the number of columns in the input DataFrame?