Problem With The Apply Threshold Function
So I tried to make a Logistical Regression on a data and when I run it the apply threshold gets an error saying:
"Predicted label is incompatible"
"The values of the predicted label does not match the model expectation. Value {0} unknown".
"Predicted label is incompatible"
"The values of the predicted label does not match the model expectation. Value {0} unknown".
Find more posts tagged with
Sort by:
1 - 2 of
21
Sort by:
1 - 2 of
21
Hi @SelaTheRock,
In attached file, the working process...
There was an inconsistency between the setting of classes in Create Threshold operator (Yes, No) and the expected output of the label (true/false).
Moreover I done several modifications in your process in order to become more relevant.
To go further, you have an highly imbalanced dataset (true = 7654 / false = 58), that 's why your logistic regression model predicts only
the class = True.
I think it's not what you want.
To improve your process, you can use the Smote Upsampling operator as a preprocessing step in order to increase artificially your minority class(false). by this way you will be able to predict correctly your minority class...
Good luck !
Regards,
Lionel
In attached file, the working process...
There was an inconsistency between the setting of classes in Create Threshold operator (Yes, No) and the expected output of the label (true/false).
Moreover I done several modifications in your process in order to become more relevant.
To go further, you have an highly imbalanced dataset (true = 7654 / false = 58), that 's why your logistic regression model predicts only
the class = True.
I think it's not what you want.
To improve your process, you can use the Smote Upsampling operator as a preprocessing step in order to increase artificially your minority class(false). by this way you will be able to predict correctly your minority class...
Good luck !
Regards,
Lionel
In attached file, the working process...
There was an inconsistency between the setting of classes in Create Threshold operator (Yes, No) and the expected output of the label (true/false).
Moreover I done several modifications in your process in order to become more relevant.
To go further, you have an highly imbalanced dataset (true = 7654 / false = 58), that 's why your logistic regression model predicts only
the class = True.
I think it's not what you want.
To improve your process, you can use the Smote Upsampling operator as a preprocessing step in order to increase artificially your minority class(false). by this way you will be able to predict correctly your minority class...
Good luck !
Regards,
Lionel