Pandas returns this: ValueError: Unknown label type: 'continuous'

Question

I am having trouble when using pandas and sklearn for machine learning. My problem is

ValueError: Unknown label type: 'continuous'

I tried

model = sklearn.tree.DecisionTreeClassifier()
model.fit(X, y)

and it returns this error:

ValueError                                Traceback (most recent call last)
<ipython-input-45-3caead2f350b> in <module>
----> 1 model.fit(ninp, out)

c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    888         """
    889 
--> 890         super().fit(
    891             X, y,
    892             sample_weight=sample_weight,

c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    179 
    180         if is_classification:
--> 181             check_classification_targets(y)
    182             y = np.copy(y)
    183 

c:\users\user\appdata\local\programs\python\python38-32\lib\site-packages\sklearn\utils\multiclass.py in check_classification_targets(y)
    170     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
    171                       'multilabel-indicator', 'multilabel-sequences']:
--> 172         raise ValueError("Unknown label type: %r" % y_type)
    173 
    174 

ValueError: Unknown label type: 'continuous'

Answer 1

A classifier classifies a set of examples into discrete classes (i.e. it assigns a label corresponding to one of K classes). If your target (the content of your y variable) is continuous (for example a float ranging between 0 and 1), then the decision tree does not know what to do with it.

You have 2 solutions:

1. Your problem is a classification task and you need to model your target variable so that it represents categories and not a continuous variable
2. Your problem is not a classification task, it is a regression task and you need to use the appropriate models (e.g. DecisionTreeRegressor)