I am having a hard time evaluating my model of imputation.
I used an iterative imputer model to fill in the missing values in all four columns.
For the model on the iterative imputer, I am using a Random forest model, here is my code for imputing:
imp_mean = IterativeImputer(estimator=RandomForestRegressor(), random_state=0) imp_mean.fit(my_data) my_data_filled= pd.DataFrame(imp_mean.transform(my_data)) my_data_filled.head()
My problem is how can I evaluate my model. How can I know if the filled values are right?
I used a describe function before and after filling in the missing values it gives me nearly the same mean and std. Also, the correlation between variables stayed nearly the same with slight changes.