WeltGuidesPredict a number
Predict a number
Classification predicts a label; regression predicts a numeric target. Here you fit a
Regressor on real Yacht data and measure its errors on held-out rows.
You'll need
- The Yacht train and test tables from the complete Yacht walkthrough. It downloads the real research table, prints its rows and schema, and splits train and test rows.
Fit and measure#
Fit a Regressor, predict the held-out rows, then calculate MAE, RMSE and R²:
# Continue after preparing the Yacht train/test tables in Start.
from welt import Regressor
from sklearn.metrics import mean_absolute_error, root_mean_squared_error, r2_score
model = Regressor(model="tabicl-v2", random_state=9)
model.fit(train, target=target)
predictions = model.predict(query)
print("Test MAE:", mean_absolute_error(y_test, predictions))
print("Test RMSE:", root_mean_squared_error(y_test, predictions))
print("Test R²:", r2_score(y_test, predictions))
- MAE and RMSE use the recorded target's units; smaller is better.
- R² can be negative.
Compare actual and predicted rows to understand the misses.
Your measurements, not a benchmark
These are held-out measurements, not a publisher benchmark or a guarantee. If you tune, use a separate validation split.
What a regressor returns#
predict returns finite mean points in the target's units. It does not return class
probabilities, calibrated intervals or conformal coverage.
A task-specific available regression profile is required; there is no task or model fallback. Predictors keep their original task, version and configuration.
Next steps#
- Notebook examples the full regression recipe in a notebook.
- Reuse a predictor reopen a fitted predictor without another fit.
- Use your own table run the same workflow on your data.