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edited Mar 14, 2020 at 8:46

Lucas Morin

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Added code block

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edit approved Sep 25, 2015 at 7:39

Dawny33

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I am training and predicting on the same data set-set, but I want to perform 10 fold-fold cross validation-validation and predict on the left out fold and thus predict on the whole data set. How can I do this? Currently

The libraries which I am using from sklearn import cross_validation import xgboost as xgb are:

from sklearn import cross_validation
import xgboost as xgb

I am training and predicting on the same data set but I want to perform 10 fold cross validation and predict on the left out fold and thus predict on the whole data set. How can I do this? Currently I am using from sklearn import cross_validation import xgboost as xgb

I am training and predicting on the same data-set, but I want to perform 10-fold cross-validation and predict on the left out fold and thus predict on the whole data set. How can I do this?

The libraries which I am using are:

from sklearn import cross_validation
import xgboost as xgb

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asked Aug 20, 2015 at 9:53

Ved Gupta

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Is there a way of performing stratified cross validation using xgboost module in python?

I am training and predicting on the same data set but I want to perform 10 fold cross validation and predict on the left out fold and thus predict on the whole data set. How can I do this? Currently I am using from sklearn import cross_validation import xgboost as xgb

python scikit-learn cross-validation

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Is there a way of performing stratified cross validation using xgboost module in python?