#NUElectures 22 WILLKOMMEN BEI DEN - Referent: Prof. Dr. Patrick Zschech
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
WILLKOMMEN BEI DEN #NUElectures 22 Referent: Prof. Dr. Patrick Zschech WISSENSCHAFTSVORLESUNGEN IM ZENTRUM NÜRNBERGS: www.nuelectures.de
Prof. Dr. Martin Matzner Sprecher des Instituts für Wirtschaftsinformatik am Fachbereich Wirtschafts- und Sozialwissenschaften
#NUElecture bietet eine bürgernahe Plattform für die Wissenschaft, um aktiv in die Stadtgesellschaft hinein zu kommunizieren und Anregungen aus ihr aufzugreifen. Forschende der WiSo Nürnberg stellen innovative Forschung erlebbar, verständlich und spannend an diversen Orten in der Stadt vor. Die öffentlichen Vorträge und Diskussionsforen behandeln Themen von hoher gesellschaftlicher Relevanz aus dem Spektrum der Forschungsschwerpunkte der #FAUWiSo. WISSENSCHAFTSVORLESUNGEN IM ZENTRUM NÜRNBERGS: www.nuelectures.de
Die Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg ist vernetzter Innovationsführer im weltweiten Wettbewerb FAU und Innovation Platz 1 in Deutschland & Platz 20 weltweit im QS World Die Nürnberger Schule University Ranking 2022 nach vernetzt seit den 1920ern Zitationen per Fakultät Wirtschafts- und Sozialwissenschaften. Quelle: QS World University Ranking, Stand: 2021 FAU Erlangen-Nürnberg Fachbereich Wirtschafts- Die WI in Nürnberg vernetzt seit Platz 1 in Deutschland & und Sozialwissenschaften 1970 Ökonomie und Informatik. Platz 14 weltweit laut Reuters- Volluniversität mit 5 Fakultäten Fachbereich mit 8 Instituten 13 Professorinnen und 604 Professorinnen & Professoren Rangliste der innovativsten über 50 Lehrstühle & Professuren Universitäten Professoren sind vernetzt ca. 40.000 Studierende ca. 6.500 Studierende Standorte in 5 Städten in der WI. Quelle: Reuters, Stand: 2019 Bild: FAU/Celina Henning; https://www.fau.de/research/forschungsprofil/rankings/
Aktuell forschen und lehren 13 Professorinnen und Professoren am Institut für Wirtschaftsinformatik Nürnberg vertritt die Wirtschaftsinformatik in ihrer ganzen thematischen Breite Prof. Dr. Kathrin M. Möslein & Prof. Dr. Angela Roth & Hon.-Prof. Dr. Prof. Dr. Freimut Prof. Dr. Michael Prof. Dr. Martin Prof. Dr. Sven Laumer Bernhard Grill & em. Prof. Dr. Dr. h. c. mult. Peter Mertens Bodendorf Amberg Matzner Schöller-Stiftungslehrstuhl für Lehrstuhl für Wirtschaftsinformatik, Management Intelligence Lehrstuhl für Lehrstuhl für Digital Industrial Wirtschaftsinformatik, insb. insb. Innovation und Wertschöpfung Services Wirtschaftsinformatik, Service Systems Digitalisierung in Wirtschaft und insb. IT Management Gesellschaft Prof. Dr. Andreas Prof. Dr. Verena Prof. Dr. Mathias Prof. Dr. Patrick Prof. Dr. Benedikt Harth Tiefenbeck Kraus Zschech Morschheuser Lehrstuhl für Juniorprofessur für Juniorprofessur für Data Juniorprofessur für Intelligent Juniorprofessur für Wirtschaftsinformatik, insb. Digital Transformation Analytics Information Systems Wirtschaftsinformatik, Techn. Informationssysteme insbesondere Gamification
Prof. Dr. Patrick Zschech Juniorprofessur für Intelligent Information Systems am Fachbereich Wirtschafts- und Sozialwissenschaften
Prof. Dr. Patrick Zschech White-Box AI: Transparente künstliche Intelligenz FACHBEREICH WIRTSCHAFTS- UND SOZIALWISSENSCHAFTEN: www.wiso.fau.de
White-Box AI: Our research group at FAU Julian Sven Patrick Lasse Nico Mathias https://www.whitebox-ai.rw.fau.de/ www.nuelecture.de 16.11.2022 8
Machine learning models are highly beneficial to support human decision-making (Source: https://images.app.goo.gl/zc7wENjfKAAXGQcJ7) (Source: https://images.app.goo.gl/MtEqRqxLm73WahRUA) (Source: https://images.app.goo.gl/83UEoMsvvKRY3Tbm9) (Source: https://images.app.goo.gl/9HPfLoF34msByCkp7) (Source: https://images.app.goo.gl/n1dSLqetwJBKYgaFA) (Source: https://images.app.goo.gl/XsozXxRRUQ1d6vUr9) www.nuelecture.de 16.11.2022 9
BUT: Advanced machine learning models often lack transparency ▪ Possible solution: Development and evaluation of interpretable machine learning models ▪ Focus for today: Predictive modeling, supervised machine learning, tabular data www.nuelecture.de 16.11.2022 10
Predictive modeling is mostly about mapping the feature space to the target space www.nuelecture.de 16.11.2022 11
The “myth” about the trade-off between model accuracy and interpretability Predictive Performance Linear regression (LR): = + Neural networks = 389 − 6 × Tree ensembles SVM Decision tree Linear/logistic regression https://images.app.goo.gl/ZXh26jWKwHKSFG1H6 Model Interpretability Adopted from Gunning, D., and Aha, D. 2019. “DARPA’s Explainable Artificial Intelligence (XAI) Program,” AI Magazine (40:2), pp. 44–58. (https://doi.org/10.1609/aimag.v40i2.2850). www.nuelecture.de 16.11.2022 12
The “myth” about the trade-off between model accuracy and interpretability Predictive Performance Decision tree (DT): Neural networks Tree ensembles SVM Decision tree Linear/logistic regression Model Interpretability www.nuelecture.de 16.11.2022 13
The “myth” about the trade-off between model accuracy and interpretability Predictive Performance Random forest (RF): Neural networks Tree ensembles SVM Decision tree Linear/logistic regression https://images.app.goo.gl/PqsxKWUNwWRhjzTu7 Model Interpretability www.nuelecture.de 16.11.2022 14
The “myth” about the trade-off between model accuracy and interpretability Predictive Performance Gradient boosting machine (GBM): Neural networks Tree ensembles SVM Decision tree Linear/logistic regression Model Interpretability www.nuelecture.de 16.11.2022 15
The “myth” about the trade-off between model accuracy and interpretability Predictive Performance Deep neural networks / multi-layer perceptron (MLP): Neural networks Tree ensembles SVM Decision tree Linear/logistic regression https://images.app.goo.gl/JGUGviUAbcY7henZ6 Model Interpretability www.nuelecture.de 16.11.2022 16
The “myth” about the trade-off between model accuracy and interpretability Predictive Performance e.g., SHAP: Neural networks Post-hoc explanations (XAI) Tree ensembles SVM Decision tree Linear/logistic regression https://images.app.goo.gl/ZtgbuBFcUMF1PbPL9 Model Interpretability www.nuelecture.de 16.11.2022 17
The “myth” about the trade-off between model accuracy and interpretability Predictive Performance Neural networks Intrinsically interpretable models OUR FOCUS: Tree ensembles Intrinsically interpretable ML models based on constrained model structures: SVM ▪ Additivity, ▪ Linearity, Decision tree ▪ Regularization, etc. Linear/logistic regression Model Interpretability www.nuelecture.de 16.11.2022 18
Generalized additive model (GAM) ▪ In generalized additive models (GAMs), input variables are mapped independently of each other in a non-linear manner and the mappings are summed up afterward: = 1 1 + … + = + ▪ ∙ is called link function − For regression, link function = identity function − For classification, link function = logistic function ▪ is called shape function − Through the shape functions, it is directly observable how each feature affects the predicted output. = + ( ) ▪ Usually, univariate mappings for single features ▪ Some models also offer pair-wise interactions www.nuelecture.de 16.11.2022 19
Different proposals of how shape functions are learned in GAMs ▪ Traditional GAM via splines (Hastie & Tibshirani, 1986) Splines are piecewise polynomial functions that can approximate complex shapes through curve fitting. ▪ Explainable Boosting Machine (EBM) (Lou et al., 2012, 2013) ▪ Neural Additive Model (NAM) (Agarwal et al. 2021) ▪ GAMI-Net (Yang et al., 2021a) ▪ Enhanced Explainable Neural Network (ExNN) https://images.app.goo.gl/w4DfFq8fCBcWtVX66 (Yang et al., 2021b) Hastie, T., and Tibshirani, R. 1986. “Generalized Additive Models,” Statistical Science (1:3). (https://doi.org/10.1214/ss/1177013604). www.nuelecture.de 16.11.2022 20
Different proposals of how shape functions are learned in GAMs ▪ Traditional GAM via splines (Hastie & Tibshirani, 1986) EBM uses bagged and boosted decision trees ensembles for fitting complex shape functions. ▪ Explainable Boosting Machine (EBM) (Lou et al., 2012, 2013) ▪ Neural Additive Model (NAM) (Agarwal et al. 2021) ▪ GAMI-Net (Yang et al., 2021a) ▪ Enhanced Explainable Neural Network (ExNN) (Yang et al., 2021b) Lou, Y., Caruana, R., and Gehrke, J. 2012. “Intelligible Models for Classification and Regression,” in 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. www.nuelecture.de 16.11.2022 21
Different proposals of how shape functions are learned in GAMs ▪ Traditional GAM via splines (Hastie & Tibshirani, 1986) In NAM, shape functions are learned via individual ▪ Explainable Boosting Machine (EBM) deep subnetworks with (Lou et al., 2012, 2013) multiple hidden layers & specific neural units. ▪ Neural Additive Model (NAM) (Agarwal et al. 2021) ▪ GAMI-Net (Yang et al., 2021a) ▪ Enhanced Explainable Neural Network (ExNN) (Yang et al., 2021b) Agarwal, R., Melnick, L., Frosst, N., Zhang, X., Lengerich, B., Caruana, R., and Hinton, G. 2021. “Neural Additive Models: Interpretable Machine Learning with Neural Nets,” ArXiv:2004.13912 [Cs, Stat]. www.nuelecture.de 16.11.2022 22
Different proposals of how shape functions are learned in GAMs ▪ Traditional GAM via splines (Hastie & Tibshirani, 1986) GAMI-Net is similar to NAM, except that it additionally aims at identifying pair-wise interactions, which requires further ▪ Explainable Boosting Machine (EBM) constraints (e.g., heredity and marginal clarity constraints). (Lou et al., 2012, 2013) ▪ Neural Additive Model (NAM) (Agarwal et al. 2021) ▪ GAMI-Net (Yang et al., 2021a) ▪ Enhanced Explainable Neural Network (ExNN) (Yang et al., 2021b) Yang, Z., Zhang, A., and Sudjianto, A. 2021. “GAMI-Net: An Explainable Neural Network Based on Generalized Additive Models with Structured Interactions,” Pattern Recognition (120) www.nuelecture.de 16.11.2022 23
Different proposals of how shape functions are learned in GAMs ▪ Traditional GAM via splines (Hastie & Tibshirani, 1986) ExNN is based on the structure of a generalized additive index model (GAIM) using a projection layer that fully connects all input features to the following subnets. ▪ Explainable Boosting Machine (EBM) (Lou et al., 2012, 2013) ▪ Neural Additive Model (NAM) (Agarwal et al. 2021) ▪ GAMI-Net (Yang et al., 2021a) ▪ Enhanced Explainable Neural Network (ExNN) (Yang et al., 2021b) Yang, Z., Zhang, A., and Sudjianto, A. 2021. “Enhancing Explainability of Neural Networks Through Architecture Constraints,” IEEE Transactions on Neural Networks and Learning Systems (32:6), pp. 2610–2621. www.nuelecture.de 16.11.2022 24
Research questions ▪ Beyond individual model proposals, it currently lacks a neutral, independent, overarching cross-model comparison: ▪ RQ1: Can interpretable models based on additive model constraints provide competitive prediction results as compared to traditional white-box and black-box ML models? ▪ RQ2: How do the outputs of interpretable ML models based on additive model constraints differ from each other objectively? www.nuelecture.de 16.11.2022 25
Collection of benchmark datasets from Kaggle and UCI ML Repository Features Type Dataset Instances Prediction task (num/cat) Water potability 3,276 9/0 Will the water be safe for consumption? Stroke 5,110 3/7 Will a patient suffer from a stroke? Telco churn 7,043 3/16 Will a customer leave the company? Classification FICO credit score 10,459 21/2 Will a client repay within 2 years? Adult 32,561 7/8 Will the income exceed $50.000/year? Bank marketing 45,211 5/11 Will a client subscribe to a deposit? Airline satisfaction 103,904 18/4 Will a passenger be satisfied? Car price 205 13/11 What is the price of a car? Student grade 649 13/17 What is a student’s final grade? Regression Crimes 1,994 99/0 How many violent crimes will happen? Bike rental 17,379 6/6 How many bikes will be rented/hour? California housing 20,640 8/0 What is the value of a house? www.nuelecture.de 16.11.2022 26
Evaluation of predictive performance (RQ1) Interpretable Models Black-box Models Dataset Splines EBM NAM GAMI-Net ExNN LR DT RF GBM XGB MLP Water* 0.557 0.631 0.469 0.634 0.632 0.464 0.609 0.548 0.633 0.642 0.646 Stroke* 0.928 0.927 0.928 0.928 0.927 0.928 0.917 0.928 0.928 0.930 0.928 Telco* 0.800 0.797 0.723 0.799 0.787 0.799 0.747 0.780 0.790 0.782 0.790 FICO* 0.725 0.725 0.619 0.728 0.709 0.718 0.667 0.716 0.722 0.710 0.716 Adult* 0.854 0.866 0.727 0.854 0.851 0.846 0.846 0.827 0.867 0.868 0.850 Bank* 0.893 0.895 0.833 0.893 0.899 0.888 0.892 0.859 0.899 0.899 0.898 Airline* 0.935 0.945 0.773 0.934 0.951 0.875 0.950 0.921 0.958 0.963 0.958 Car** 0.132 0.083 1.112 1.001 0.129 0.089 0.148 0.093 0.098 0.097 0.106 Student** 0.732 0.730 1.239 1.000 1.390 0.731 1.239 0.712 0.757 0.800 0.781 Crimes** 0.503 0.311 1.152 0.388 0.511 0.312 0.620 0.319 0.320 0.343 0.351 Bike** 0.182 0.060 1.214 0.145 0.114 0.499 0.08 0.202 0.045 0.041 0.079 Housing** 0.242 0.181 1.131 0.266 0.223 0.352 0.261 0.357 0.171 0.163 0.214 *Classification performance measured with F1-score ** Regression performance measure with mean square error (MSE) www.nuelecture.de 16.11.2022 28
Evaluation of predictive performance (RQ1) Interpretable Models Black-box Models Dataset Splines EBM NAM GAMI-Net ExNN LR DT RF GBM XGB MLP Water* 0.557 0.631 0.469 0.634 0.632 0.464 0.609 0.548 0.633 0.642 0.646 Stroke* 0.928 0.927 0.928 0.928 0.927 0.928 0.917 0.928 0.928 0.930 0.928 Telco* 0.800 0.797 0.723 0.799 0.787 0.799 0.747 0.780 0.790 0.782 0.790 FICO* 0.725 0.725 0.619 0.728 0.709 0.718 0.667 0.716 0.722 0.710 0.716 Adult* 0.854 0.866 0.727 0.854 0.851 0.846 0.846 0.827 0.867 0.868 0.850 Bank* 0.893 0.895 0.833 0.893 0.899 0.888 0.892 0.859 0.899 0.899 0.898 Airline* 0.935 0.945 0.773 0.934 0.951 0.875 0.950 0.921 0.958 0.963 0.958 Car** 0.132 0.083 1.112 1.001 0.129 0.089 0.148 0.093 0.098 0.097 0.106 Student** 0.732 0.730 1.239 1.000 1.390 0.731 1.239 0.712 0.757 0.800 0.781 Crimes** 0.503 0.311 1.152 0.388 0.511 0.312 0.620 0.319 0.320 0.343 0.351 Bike** 0.182 0.060 1.214 0.145 0.114 0.499 0.080 0.202 0.045 0.041 0.079 Housing** 0.242 0.181 1.131 0.266 0.223 0.352 0.261 0.357 0.171 0.163 0.214 *Classification performance measured with F1-score ** Regression performance measure with mean square error (MSE) www.nuelecture.de 16.11.2022 29
With some overlap, GAMs achieved best results in Evaluation of predictive performance (RQ1) 5 out of 12 datasets. Interpretable Models Black-box Models Dataset Splines EBM NAM GAMI-Net ExNN LR DT RF GBM XGB MLP Water* 0.557 0.631 0.634 0.632 0.464 0.609 0.548 0.633 0.642 0.646 Stroke* 0.928 0.927 0.928 0.927 0.928 0.917 0.928 0.928 0.930 0.928 Telco* 0.800 0.797 0.799 0.787 0.799 0.747 0.780 0.790 0.782 0.790 FICO* 0.725 0.725 0.728 0.709 0.718 0.667 0.716 0.722 0.710 0.716 Adult* 0.854 0.866 0.854 0.851 0.846 0.846 0.827 0.867 0.868 0.850 Bank* 0.893 0.895 0.893 0.899 0.888 0.892 0.859 0.899 0.899 0.898 EBM best performing Airline* model interpretable 0.935 0.945 0.934 0.951 0.875 0.950 0.921 0.958 0.963 0.958 XGB overall Car** in 6 out of 12 tasks 0.132 0.083 1.001 0.129 0.089 0.148 0.093 0.098 0.097 best 0.106 model in Student** 0.732 0.730 LR model 1.000achieves 1.390 0.731 1.239 0.712 0.757 0.800 60.781 out of 12 Crimes** 0.503 0.311 second-highest 0.388 0.511 0.312 0.620 0.319 0.320 0.343 0.351 performance in Bike** 0.182 0.060 0.145 0.114 0.499 0.080 0.202 0.045 0.041 0.079 3 out of 12 tasks. Housing** 0.242 0.181 0.266 0.223 0.352 0.261 0.357 0.171 0.163 0.214 *Classification performance measured with F1-score ** Regression performance measure with mean square error (MSE) www.nuelecture.de 16.11.2022 30
Evaluation of training times (measured in seconds) Interpretable Models Black-box Models Dataset Splines EBM NAM GAMI-Net ExNN LR DT RF GBM XGB MLP Water* 0.15 1.51 32.51 11.74 0.02 0.01 0.22 0.66 0.11 0.26 Stroke* 1.23 0.69 46.70 23.36 0.01 0.01 0.11 0.44 0.08 0.43 Telco* 2.09 1.51 57.51 16.72 0.02 0.02 0.15 0.90 0.12 0.66 FICO* 2.37 1.66 95.37 14.52 0.03 0.04 0.27 1.74 0.12 0.95 Adult* 38.54 21.64 519.72 44.04 0.10 0.07 0.45 3.48 0.46 3.34 Bank* 15.45 9.49 777.13 63.40 0.12 0.09 0.61 3.97 0.47 4.33 Airline* 17.75 31.02 627.22 225.09 0.10 0.21 2.02 12.07 0.72 9.47 Car** 0.68 1.83 6.99 8.06 0.01 0.01 0.08 0.05 0.04 0.02 Student** 0.41 0.44 16.01 7.91 0.01 0.01 0.10 0.09 0.05 0.06 Crimes** 3.04 1.57 92.95 9.24 0.01 0.05 1.32 1.91 0.12 0.20 Bike** 0.84 3.82 299.09 42.54 0.01 0.03 0.85 1.27 0.15 1.42 Housing** 0.18 6.97 418.85 46.38 0.01 0.06 1.88 2.83 0.30 1.42 www.nuelecture.de 16.11.2022 32
Evaluation of interpretability (RQ2) – Adult dataset (target: income>50k$) Splines EBM GAMI-Net Feature: age EBM captures detailed patterns. Feature: capital_gain Feature plots of GAMI-Net are smoother and thus appear more appealing for interpretation purposes. www.nuelecture.de 16.11.2022 33
Evaluation of interpretability (RQ2) – Adult dataset (target: income>50k$) NAM ExNN ExNN is hardly interpretable when many features are involved. NAM showed extremely jagged behavior in our experiments due to overfitting. www.nuelecture.de 16.11.2022 34
Merits and limitations of related work Model Feature Shapes Model Compactness Controllability Training Effort Controllable sparsity, Simple configuration, Splines Smooth behavior Low complexity controllable smoothing medium training times Piecewise constant Increased complexity Controllable number Simple configuration, EBM with sharp jumps due to missing sparsity of interactions fast training times Controllable regularization Extensive HP tuning, NAM Jagged behavior Low complexity for limiting jagged behavior slow training times Controllable sparsity, Moderate HP tuning, GAMI-Net Smooth behavior Low complexity controllable smoothness, moderate training times controllable interactions Smooth behavior High complexity due Controllable sparsity, Moderate HP tuning, ExNN of feature projections to feature projections controllable smoothness moderate training times www.nuelecture.de 16.11.2022 35
Merits and limitations of related work Model Feature Shapes Model Compactness Controllability Training Effort Controllable sparsity, Simple configuration, Splines Smooth behavior Low complexity controllable smoothing medium training times Increased complexity Piecewise constant Controllable number Simple configuration, EBM due to missing with sharp jumps of interactions fast training times sparsity Controllable regularization Extensive HP tuning, NAM Jagged behavior Low complexity for limiting jagged behavior slow training times Controllable sparsity, Moderate HP tuning, GAMI-Net Smooth behavior Low complexity controllable smoothness, moderate training times controllable interactions Smooth behavior High complexity due Controllable sparsity, Moderate HP tuning, ExNN of feature projections to feature projections controllable smoothness moderate training times www.nuelecture.de 16.11.2022 36
Outlook: The vision of our White-Box AI group Model restrictions with impact Domain expert knowledge Interpretation on shape functions and physical constraints ▪ Inspection and comparison with own expert knowledge Shape functions ▪ Detection of error sources IGANN Feature 1 Feature 2 during modeling (e.g., height) (e.g., age) Model 1 ▪ Detection of biases and Model 1: Linear shape discrimination functions ▪ Acquisition of new insights Model 2: Feature 1 Model 2 through interpretation of with upper bound the shape functions Model 3 Model 3: Unrestricted ▪ … shape functions www.nuelecture.de 16.11.2022 37
Don‘t hesitate to contact me! Patrick Zschech patrick.zschech@fau.de Thank you very much for your attention! FAU Erlangen-Nürnberg Assistant Professor for Intelligent Information Systems Relevant publications/submissions for this study — Zschech P., Weinzierl S., Hambauer N., Zilker S., Kraus M. (2022) GAM(e) Changer or Not? An Evaluation of Interpretable Machine Learning Models Based on Additive Model Constraints. In: Proceedings of the 30th European Conference on Information Systems (ECIS), Timișoara, Romania. (https://aisel.aisnet.org/ecis2022_rp/106/) — Kraus M., Tschernutter D., Weinzierl S., Zschech P. (2023) IGANN: Interpretable Generalized Additive Neural Networks. Submitted to European Journal of Operational Research (EJOR). www.nuelecture.de 16.11.2022 38
References Agarwal, R., Melnick, L., Frosst, N., Zhang, X., Lengerich, B., Caruana, R., and Hinton, G. 2021. “Neural Additive Models: Interpretable Machine Learning with Neural Nets,” ArXiv:2004.13912 [Cs, Stat]. (http://arxiv.org/abs/2004.13912). Gunning, D., and Aha, D. 2019. “DARPA’s Explainable Artificial Intelligence (XAI) Program,” AI Magazine (40:2), pp. 44–58. (https://doi.org/10.1609/aimag.v40i2.2850). Hastie, T., and Tibshirani, R. 1986. “Generalized Additive Models,” Statistical Science (1:3). (https://doi.org/10.1214/ss/1177013604). Lou, Y., Caruana, R., and Gehrke, J. 2012. “Intelligible Models for Classification and Regression,” in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’12, Beijing, China: ACM Press, p. 150. (https://doi.org/10.1145/2339530.2339556). Lou, Y., Caruana, R., Gehrke, J., and Hooker, G. 2013. “Accurate Intelligible Models with Pairwise Interactions,” in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago Illinois USA: ACM, August 11, pp. 623–631. (https://doi.org/10.1145/2487575.2487579). Nori, H., Jenkins, S., Koch, P., and Caruana, R. 2019. “InterpretML: A Unified Framework for Machine Learning Interpretability,” ArXiv:1909.09223 [Cs, Stat]. (http://arxiv.org/abs/1909.09223). Vaughan, J., Sudjianto, A., Brahimi, E., Chen, J., and Nair, V. N. 2018. “Explainable Neural Networks Based on Additive Index Models,” ArXiv:1806.01933 [Cs, Stat]. (http://arxiv.org/abs/1806.01933). Yang, Z., Zhang, A., and Sudjianto, A. 2021a. “Enhancing Explainability of Neural Networks Through Architecture Constraints,” IEEE Transactions on Neural Networks and Learning Systems (32:6), pp. 2610–2621. (https://doi.org/10.1109/TNNLS.2020.3007259). Yang, Z., Zhang, A., and Sudjianto, A. 2021b. “GAMI-Net: An Explainable Neural Network Based on Generalized Additive Models with Structured Interactions,” Pattern Recognition (120), p. 108192. (https://doi.org/10.1016/j.patcog.2021.108192). www.nuelecture.de 16.11.2022 39
Backup
Multi-layer perceptron, gradient descent, and backpropagation https://images.app.goo.gl/FNRtxcMf1k6rd2cr7 www.nuelecture.de 16.11.2022 41
Example of how to read the prediction from a gradient boosting algorithm www.nuelecture.de 16.11.2022 42
You can also read
