Compare Models
Compare models
Cross-validation messages
⏩ stepit 'cross_validate_pipe': Starting execution of `strom.modelling.cross_validate_pipe()` 2025-11-24 03:26:34 [Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 2.1s finished ✅ stepit 'cross_validate_pipe': Successfully completed and cached [exec time 2.1 seconds, cache time 0.0 seconds, size 2.2 KB] `strom.modelling.cross_validate_pipe()` 2025-11-24 03:26:36 ♻️ stepit 'cross_validate_pipe': is up-to-date. Using cached result for `strom.modelling.cross_validate_pipe()` 2025-11-24 03:26:36 ♻️ stepit 'cross_validate_pipe': is up-to-date. Using cached result for `strom.modelling.cross_validate_pipe()` 2025-11-24 03:26:36
Metrics
Single split
Metrics based on the test set of the single split
Cross validation
Predictions, residuals, observed
next
Time vs. Predicted and Observed
Time vs. Residuals
Model details
Pipeline(steps=[('vars', ColumnSelector(columns=['tt_tu_mean', 'tf_std_mean'])),
('polynomial', PolynomialFeatures(degree=4)),
('model', LinearRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
| steps | [('vars', ...), ('polynomial', ...), ...] | |
| transform_input | None | |
| memory | None | |
| verbose | False |
Parameters
| columns | ['tt_tu_mean', 'tf_std_mean'] |
Parameters
| degree | 4 | |
| interaction_only | False | |
| include_bias | True | |
| order | 'C' |
Parameters
| fit_intercept | True | |
| copy_X | True | |
| tol | 1e-06 | |
| n_jobs | None | |
| positive | False |
Pipeline(steps=[('vars', ColumnSelector(columns=['tt_tu_mean', 'td_mean'])),
('model',
GradientBoostingRegressor(max_depth=5, min_samples_leaf=5,
min_samples_split=48,
n_estimators=60, random_state=7,
subsample=1))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
| steps | [('vars', ...), ('model', ...)] | |
| transform_input | None | |
| memory | None | |
| verbose | False |
Parameters
| columns | ['tt_tu_mean', 'td_mean'] |
Parameters
| loss | 'squared_error' | |
| learning_rate | 0.1 | |
| n_estimators | 60 | |
| subsample | 1 | |
| criterion | 'friedman_mse' | |
| min_samples_split | 48 | |
| min_samples_leaf | 5 | |
| min_weight_fraction_leaf | 0.0 | |
| max_depth | 5 | |
| min_impurity_decrease | 0.0 | |
| init | None | |
| random_state | 7 | |
| max_features | None | |
| alpha | 0.9 | |
| verbose | 0 | |
| max_leaf_nodes | None | |
| warm_start | False | |
| validation_fraction | 0.1 | |
| n_iter_no_change | None | |
| tol | 0.0001 | |
| ccp_alpha | 0.0 |
Pipeline(steps=[('vars',
ColumnSelector(columns=['tt_tu_mean', 'vp_std_mean',
'tf_std_mean'])),
('model', LinearSVR(random_state=7))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
| steps | [('vars', ...), ('model', ...)] | |
| transform_input | None | |
| memory | None | |
| verbose | False |
Parameters
| columns | ['tt_tu_mean', 'vp_std_mean', ...] |
Parameters
| epsilon | 0.0 | |
| tol | 0.0001 | |
| C | 1.0 | |
| loss | 'epsilon_insensitive' | |
| fit_intercept | True | |
| intercept_scaling | 1.0 | |
| dual | 'auto' | |
| verbose | 0 | |
| random_state | 7 | |
| max_iter | 1000 |