types - Error while testing DecisionTreeClassifier in Scikit Learn with Python -

i read data in csv file, first line strings , rest decimals. had convert data file string decimal , trying run decision tree classifier on data. can train data fine when call decisiontreeclassifier.score() error message: "unknown not supported"

here code:

cval = kfold(len(file)-1, n_folds=10, shuffle=true); train_index, test_index in cval:     obfa_train, obfa_test = np.array(obfa)[train_index], np.array(obfa)[test_index]     ttime_train, ttime_test = np.array(ttime)[train_index], np.array(ttime)[test_index]     model = tree.decisiontreeclassifier()     model = model.fit(obfa_train.tolist(), ttime_train.tolist())     print model.score(obfa_test.tolist(), ttime_test.tolist()) 

i filled obfa , ttime these lines earlier:

ttime.append(decimal(file[i][11].strip('"'))) obfa[i-1][j-1] = decimal(file[i][j].strip('"')) 

so obfa 2d array , ttime 1d. tried removing "tolist()" in above code, did not affect error. here error report prints:

in <module>() ---> print model.score(obfa_test.tolist(), ttime_test.tolist())  in score(self, x, y, sample_weight)     """     .metrics import accuracy_score  -->return accuracy_score(y, self.predict(x), sample_weight=sample_weight)  in accuracy_score(y_true, y_pred, normalize, sample_weight)     # compute accuracy each possible representation   ->y_type, y_true, y_pred = _check_clf_targets(y_true, y_pred)     if y_type == 'multilabel-indicator':         score = (y_pred != y_true).sum(axis=1) == 0  in _check_clf_targets(y_true, y_pred)     if (y_type not in ["binary", "multiclass", "multilabel-indicator", "multilabel-sequences"]):         -->raise valueerror("{0} not supported".format(y_type))     if y_type in ["binary", "multiclass"]:  valueerror: unknown not supported 

i added print statements check dimensions of input parameters printed:

obfa_test.shape: (48l, 12l) ttime_test.shape: (48l,) 

i confused why error report shows 3 required parameters score() documentation has 2. "self" parameter? can me solve error?

this seems reminiscent of error discussed here. problem seems stem datatype you're using fit , score model. instead of decimal when filling input data arrays, try float. , don't have inaccurate answer -- can't use floats/continuous values decisiontreeclassifiers. if want use floats, use decisiontreeregressor. otherwise, try using integers or strings (but might steering away task you're trying accomplish).

as self question @ end, syntactic idiosyncrasy of python. when model.score(...), python sort of treating score(model, ...). i'm afraid don't know more right now, isn't necessary answer original question. here's answer better addresses particular question.