It looks like sklearn requires the data shape of (row number, column number). If your data shape is (row number, ) like (999, ), it does not work. By using numpy.reshape(), you should change the shape of the array to (999, 1), e.g. using In my case, it worked with that.
I know I could implement a root mean squared error function like this: What I’m looking for if this rmse function is implemented in a library somewhere, perhaps in scipy or scikit-learn?
hello i am new to sklearn in python and iam trying to learn it and use this module to predict some numbers based on two features here is the error i am getting: ValueError: only 2 non-keyword arguments accepted and here is my code:
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