Effect of a few wrongly scaled feature values on logistic regression model I was given a situation to predict the validity of the logistic regression model when it was found that certain values of a heavily weighted feature were found to be erroneously multiplied by 1000. For example, the feature has values 20.000, 78.000, 56.000, 10.000 and so on. However, few values were found to be missing the decimal point and were therefore scaled up by 1000.
Will the results of the previously fit model be valid in such a situation and why ? Also, how will the model be affected by the wrongly scaled up values.
In my opinion, the model will still be valid because even if the range of the predictor variable is high in the case of Logistic Regression, the algorithm itself will improvise itself by reducing the value of the &quot;coefficient&quot; corresponding to this variable.
You can read more about this here and here
Dhanyawad.