scikit-learn

sklearn error ValueError: Input contains NaN, infinity or a value too large for dtype(‘float64’)

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This might happen inside scikit, and it depends on what you’re doing. I recommend reading the documentation for the functions you’re using. You might be using one which depends e.g. on your matrix being positive definite and not fulfilling that criteria. EDIT: How could I miss that: is obviously wrong. Right would be: and You … Read more

sklearn error ValueError: Input contains NaN, infinity or a value too large for dtype(‘float64’)

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This might happen inside scikit, and it depends on what you’re doing. I recommend reading the documentation for the functions you’re using. You might be using one which depends e.g. on your matrix being positive definite and not fulfilling that criteria. EDIT: How could I miss that: is obviously wrong. Right would be: and

XGBoost XGBClassifier Defaults in Python

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That isn’t how you set parameters in xgboost. You would either want to pass your param grid into your training function, such as xgboost’s train or sklearn’s GridSearchCV, or you would want to use your XGBClassifier’s set_params method. Another thing to note is that if you’re using xgboost’s wrapper to sklearn (ie: the XGBClassifier() or … Read more

LogisticRegression: Unknown label type: ‘continuous’ using sklearn in python

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You are passing floats to a classifier which expects categorical values as the target vector. If you convert it to int it will be accepted as input (although it will be questionable if that’s the right way to do it). It would be better to convert your training scores by using scikit’s labelEncoder function. The … Read more