Fit function takes cat_vars variable as input but doesnot really use it.

[mankantega]

The first thing the fit function does is that it checks if all the columns are float, line 440 in the missingforest.py file. What is the point of having cat_vars then? If all the columns should be float64 why and how should one specify the cat_vars and actually use it?

[ashimb9]

Please one-hot encode the categorical variables (aka dummy encoding) so that the cat variables are represented as numeric variables. Ex: categorical variable could be 'color_name' which is either of ('red', 'blue'). One hot encoded version could be something like: 'is_color_red' in (0, 1) where 0 implies blue while 1 implies red, for instance.

[nabito]

@mankantega I've a similar experience, however in my case I tried encoding the type of categorical variables to integer and then specify cat_vars to which the function happily accepted it (same as other type such as boolean, and object type column).

@ashimb9 do you know how missingpy internally handling categorical variable? I know that many random forest based library support specialized treatment/encoding of categorical variables for best performance. Most tree-based algorithm is prone to bias/too much tree depth using one hot encoding for categorical variable (especially when there are large categories). wonder why you suggest one-hot though.

[brtang63]

@nabito It seems missingpy handles categorical variables by fitting a random forest classifier instead of a regressor, see my comment in another issue for details. @ashimb9 Is integer label encoding valid for missingpy? I think it may be more adequate than one-hot encoding in some cases. Thanks in advance.