Mar-02-2022, 08:22 AM
This will work!
import numpy as np
import pandas as pd
from sklearn.metrics import r2_score, mean_squared_error
def r2_rmse( g ):
r2 = r2_score( g['Actual'], g['Predicted'] )
rmse = np.sqrt( mean_squared_error( g['Actual'], g['Predicted'] ) )
return pd.Series( dict( r2 = r2, rmse = rmse ) )
your_df.groupby( 'Type' ).apply( r2_rmse ).reset_index()