add learning_rate_scale develop parameter

README.md

@@ -640,6 +640,7 @@ We also build on top of many great packages. Please check them out!
 - [Model Interpretability in Credit Insurance](http://hdl.handle.net/10400.5/27507)
 - [Federated Boosted Decision Trees with Differential Privacy](https://arxiv.org/pdf/2210.02910.pdf)
 - [Differentially private and explainable boosting machine with enhanced utility](https://www.sciencedirect.com/science/article/abs/pii/S0925231224011950?via%3Dihub#preview-section-abstract)
+- [Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approach](https://ieeexplore.ieee.org/abstract/document/10818483)
 - [GAM(E) CHANGER OR NOT? AN EVALUATION OF INTERPRETABLE MACHINE LEARNING MODELS](https://arxiv.org/pdf/2204.09123.pdf)
 - [GAM Coach: Towards Interactive and User-centered Algorithmic Recourse](https://arxiv.org/pdf/2302.14165.pdf)
 - [Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?](https://arxiv.org/pdf/2304.11749v1.pdf)
@@ -658,6 +659,7 @@ We also build on top of many great packages. Please check them out!
 - [Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments](https://journals.sagepub.com/doi/full/10.1177/20552076241287364)
 - [Proposing an inherently interpretable machine learning model for shear strength prediction of reinforced concrete beams with stirrups](https://pdf.sciencedirectassets.com/287527/1-s2.0-S2214509523X00035/1-s2.0-S2214509524005011/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjECUaCXVzLWVhc3QtMSJGMEQCIB0r0KsYBZufOjbCVtUtozwn1QKMdLt2tbbfhuJKjWlXAiB5Dfr7p0yyj%2FSfypTLmjPL8WbjGAB3tRACFjyyqQbbfiq8BQiu%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMqBpZ2HmN91c%2BJPqpKpAFZtvqQjCScZa4FN%2FeubsPzOk5c%2B58LliO4Zr%2Bn1pm3vtW4I9I1vA29pkhT5was1N3ccPPIm2jNLwJ%2FHiZej7A2SmFv13Ro3sTvhqG%2F6A9Xx70Nx9jOlDPJUmCypKadKp0FGfuhZQuxeN0b%2F1QUUQZG4RpxC%2FXorRRHmb%2FrXcOWBwu4PmLZAkWmTKpncjDI7oj8eh8yBe6%2FA3JkJ14ZyBgR7JnPzR2ZqMdIhvlKoyMn6EnL1Azq2y3qwEMdzSCvz3wH3sT4pClc2vPs6ruQS4CdT3E7BHrf42Q0VnUXWjuy7gt9iRr0vaWR3tD%2FxyrrEKw7XuMHO9L4rQ4Pfn1dhGZ2J8H5ocwJGSh13U5fY6noyaTNViqvHx1oHNMWL03QpkJxmUxYquBWepcDjxEc32V6eGF7Ecm8Vij3s20wdRNcHqxGFKlUCgph48CKUA79iwSGQCkWQh7bq%2FTtowTbSPud7l8xeG1MvfIVy%2B6yzrjqygvPBQs3qkvdoWUrKXe57bhr2jEkKlSdYyp2TJMD6yoYRdTPyFx5xb0KgIt6KQTPmfbqYXkd3FFz3uc0HmWC5NQz6qP9UzNcBhcK8dXo3Dw042pl0HLO1njFaa%2BBfbT89VUVUIqjrAcmHweIl1v7Eyldzr%2BGBXIlsxPO3gPzyPLF2LTggc6dA%2Bswxmgmkv%2B7n5pU5%2F5sxvEhemb%2Fqu%2B8d47O%2Bn6RH8fL4eLGGL2d0dvFvyE7gEwt%2BaU9HsIN0IHqyH5VmaTF5zaKy%2Fn%2BhkF8yGpe5Hq5yNOUGrfQgfyFn4Kqd%2FTVajxIFzk8DEY%2F%2FFtyGJ%2B8BrHV4P%2FYs8R4XcBzPQtyrTuUC1CGmF01Tc2gnnEo4pVPaIjfBk9B%2BXVMc3Mu4Ywy4L%2BsgY6sgFK3hFIXjIfoVjqrIlBvsGYaFiZB1bVKBVy3DRiBgozzYmIVhipN%2FS%2BPok1oETqvYVvLqEVkGcb5W7nUIK16lFgjwDq6ePuxdqSafgOw5jVQroNsDCPRz8B%2F4fg7kv6gs4R9SX7gCaQ2V7L6NxqJDUUqsCMtIYq05Qx43dGByqLoVEz9USpRBmTLQwpGvOmUaGNNwTsCwmt5gRP8UX3CnkwI%2FydxmhrXLEdaUIFVwJbIor9&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240604T221639Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY4E2DAHPF%2F20240604%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=eece32da8855b55208baecc0ce041e79aa03be1c292b58c67ce0215de36cbdb4&hash=46dd1da122f4cea242c6444a811fb16dde5cb8465e88552ac3eaeee97b975e9b&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2214509524005011&tid=spdf-45c1c4d1-dd97-4c0d-a04f-c30843a79e78&sid=1fea53ed2d5cf1443e4a7c4-33f4bf6475e1gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0f155c5f060d565b01055d&rr=88eb49dd2a5f7688&cc=us)
 - [Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity](https://www.nature.com/articles/s41598-024-60811-2)
+- [Interpretable Predictive Value of Including HDL-2b and HDL-3 in an Explainable Boosting Machine Model for Multiclass Classification of Coronary Artery Stenosis Severity in Acute Myocardial Infarction Patients](https://watermark.silverchair.com/ztae100.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAA2owggNmBgkqhkiG9w0BBwagggNXMIIDUwIBADCCA0wGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMnDqoUBnqG9Zyr0dAAgEQgIIDHT2M3owEzTRAV3KZzrOpzyqOYgClio-CQrzB5731fvsEe9ZWO_QfqQAKdaPyyOsEKjacd25hWs-_OvgXCqc36R4yFWu46PFOCApII2s3hbHYI1XEQozWfdyosgaQf_e7_5RIqIfwTEHt19LoYZuaDYjCqq2vmWOMZb6dNI6mz-h3Zd6BgbyYAFgRHiJfU94NU0Crf_AbbTx2jW3HqMBLYPn-ysUiyQYILNmqlKAAlw81ZjBwzusaQFsiJMCxwGyFHks7nwtnUQ8J5PU5Jelp8_fQ8x5_dlZvzvdkI9MR87zUkk4hm2XL0uyfvH92-7VV_2gMe-rU3aJZhbHJu2hENPDh_OmoDe7SOC-5EwPsgIDoDr_dgSgyhBMIbOk_TrSM4oEN6dbtvfLSDXQUWDV4semLuPjqz7WyiQz4PPt1mXuaf12X5xyVsf1Mms4UpGAKLyoCdJ-zDJ9csOPCefIsV2Bzs-KzaD63HWFLJuCU0hWIaK0QOcJATnpQb1PhFiAF6YZ_cCYTxkuAcrQyHS-WCEefNy8hB8PQXhNljtw0J499qdnLcNOM1gAQ3-o21KaTrEFs-DyvZwWmaGn8Zw1bK1CG8yVxWOh6_wjJpGjMMenstzrKFcLbJADs1yf3PuNGZds0g-Qf4NDcgsturcr0V1nLHVRFazWZhUKSeRnLjPzA5i3lVKnmwKjKa_50i0LMSIXNFS-dmvHs-qVUb8FO0_aKZ6egckXkoGG8w3Jox4MhhY2-B28Z0wbJOj8_DojCCtAmAPC0T5emRsuk1rkuRXIoMtFDWN0l7fr7RVkuy1TEd3mpa5UuU7Qo-wu_yqi6ibwLupjGeVN__7SeteoBSh8yFJgYN4BEiYmdkEX7DgKaMC90h5GakNJ7zeAPR9PFnQVRORoof04qMWK4aGod2igso1-qsCup-kVWmPy8zrQKlqxE4OCeqUpKQgZMUUAlFu643iuRnQuLnahXhui45TY8lS56XGCLqkwSG594lMoAXAYZ9tVFM4fAVwQJ3EWkJfHRRCWWGZfLwBPsdUnNEziGg4QIdrKhe-Fu7nLF)
 - [Estimate Deformation Capacity of Non-Ductile RC Shear Walls Using Explainable Boosting Machine](https://arxiv.org/pdf/2301.04652.pdf)
 - [Introducing the Rank-Biased Overlap as Similarity Measure for Feature Importance in Explainable Machine Learning: A Case Study on Parkinson’s Disease](https://www.researchgate.net/publication/362808061_Introducing_the_Rank-Biased_Overlap_as_Similarity_Measure_for_Feature_Importance_in_Explainable_Machine_Learning_A_Case_Study_on_Parkinson's_Disease)
 - [Targeting resources efficiently and justifiably by combining causal machine learning and theory](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768181/pdf/frai-05-1015604.pdf)

python/interpret-core/interpret/develop.py

@@ -16,6 +16,7 @@
     "intercept_learning_rate": 0.25,
     "cat_l2": 0.0,
     "min_samples_leaf_nominal": None,
+    "learning_rate_scale": 1.0,
     "max_cat_threshold": 9223372036854775807,
     "cat_include": 1.0,
     "purify_boosting": False,

python/interpret-core/interpret/glassbox/_ebm/_boost.py

@@ -179,11 +179,15 @@ def boost(
                             dtype=np.int32,
                         )
 
+                    learning_rate_local = learning_rate
+                    if contains_nominals and len(term_features[term_idx]) == 1:
+                        learning_rate_local *= develop.get_option("learning_rate_scale")
+
                     avg_gain = booster.generate_term_update(
                         rng,
                         term_idx=term_idx,
                         term_boost_flags=term_boost_flags_local,
-                        learning_rate=learning_rate,
+                        learning_rate=learning_rate_local,
                         min_samples_leaf=min_samples_leaf_local,
                         min_hessian=min_hessian,
                         reg_alpha=reg_alpha,