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I have discrete values in the target variable(Exactly 13 different values in total) . When I am giving that as input to Random forest Classifier ,it gives error that input as continuous. And if I give it to regressor it is predicting a value between the discrete values. How can I treat this problem

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  • $\begingroup$ Check the documentation for the software you’re using. A continuous response variable might be the default. $\endgroup$
    – Dave
    Mar 26, 2020 at 16:18
  • $\begingroup$ What are the 13 values? $\endgroup$
    – Ben Reiniger ♦
    Mar 26, 2020 at 16:27
  • $\begingroup$ Are you sure it’s a problem that the model is not predicting an integer? Poisson regression, for instance, has integer $y$ values but does necessarily predict integers. Ditto for logistic regression. $\endgroup$
    – Dave
    Sep 10, 2021 at 10:48

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I suspect the problem is that your discrete values are non-integer floats. The classifier's fit method runs type_of_target, which returns (in part):

'continuous': y is an array-like of floats that are not all integers, and is 1d or a column vector.

(source)

(Tracing the source code wasn't straightforward; a traceback of the error from you would have been helpful. For reference here, RandomForestClassifier inherits from BaseForest, whose fit method calls _validate_y_class_weight which (in the ForestClassifier child class) calls check_classification_targets which calls type_of_target.)

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