1
Posted by jag •
Noted. Thank you.
1
Posted by jag •
Found the solution. I had to run
aqora upload to get the use case data. All is good.1
Posted by jag •
Thank you. In the final stretch. 🏃♂️
2
Posted by jag •
I'm interested to know too. :)
1
Posted by jag • (edited)
@grossim I've trained my algorithm and these are the results I'm getting
RMSE: 142.9157
MAE: 60.2217
MAPE: 951165619279107584.00%
R²: 0.9240
The R² score seems good but the MAPE is still very large.
I'm computing the metrics as follows:
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score
mse = mean_squared_error(y_targets, y_preds)
rmse = np.sqrt(mse)
mae = mean_absolute_error(y_targets, y_preds)
mape = mean_absolute_percentage_error(np.abs(y_targets), np.abs(y_preds))
r2 = r2_score(y_targets, y_preds)
print(f"RMSE: {rmse:.4f}")
print(f"MAE: {mae:.4f}")
print(f"MAPE: {mape:.2%}")
print(f"R²: {r2:.4f}")I think R² could be a better metric to gauge the algorithm. R² ranges from 0 to 1, where 1 indicates a perfect fit of the algorithm to the data. On the other hand, MAPE seems to be giving these large numbers.