Aplr

Latest version: v10.7.3

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10.7.3

Fixed a bug in the calculation of the negative gradient for the `group_mse` and `group_mse_cycle` loss functions.

10.7.2

This update improves the ability to sequentially train linear effects, then non-linear effects, and finally interaction effects. While the default hyperparameters do not follow this sequence, it can be enabled using the `num_first_steps_with_linear_effects_only` and `boosting_steps_before_interactions_are_allowed` parameters.

With this update, at each stage, the algorithm now selects the boosting step with the lowest validation error before progressing to the next stage. This enhancement helps prevent overfitting and allows for a fully fitted linear model before moving on to non-linear and interaction effects. This approach can improve interpretability, as linear effects are typically easier to understand than non-linear effects, and interactions are often the most complex to interpret.

10.7.1

This release includes a minor bugfix for the `set_intercept` method. The method now ensures that `get_term_coefficients` returns the updated intercept, reflecting any adjustments made.

10.7.0

The new `set_intercept` method in APLRRegressor enables users to manually adjust the model intercept after fitting, which is useful for calibration purposes. The API reference has been updated accordingly, and the README has been rewritten for clarity, with contact details added.

10.6.4

A bug affecting the combination of `loss_function = "weibull"` and `validation_tuning_metric = "default"` has been resolved. Previously, in this combination, the case where the response contained zeros was not correctly handled, causing the fitting procedure to terminate. A workaround, which involved adding a small constant to the response, was required. This workaround is no longer necessary, as the issue has now been fixed.

10.6.3

Now using a normalized gini when _validation_tuning_metric_ is set to _negative_gini_.

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