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Huolala Safety Emergency Response Center
Huolala Safety Emergency Response Center
Apr 15, 2026 · Information Security

How to Auto‑Label 10K APIs with 95% Confidence Using Self‑Learning Feature Engineering

This article presents a detailed case study of how a large‑scale API security team built an automated, self‑learning classification system that tags tens of thousands of APIs with business labels, improves model accuracy by five points, and maintains high precision through a confidence‑driven feedback loop.

API SecurityCatBoostMachine Learning
0 likes · 13 min read
How to Auto‑Label 10K APIs with 95% Confidence Using Self‑Learning Feature Engineering
Huolala Tech
Huolala Tech
Apr 15, 2026 · Information Security

How We Built a Self‑Learning API Classification System for Security

This article details a real‑world case study of how a large logistics platform created an automated, self‑evolving API asset‑classification pipeline—covering data collection, feature engineering, model training with CatBoost, confidence‑based label feedback, and lessons learned—to improve API security monitoring and reduce manual labeling effort.

API SecurityCatBoostSHAP
0 likes · 13 min read
How We Built a Self‑Learning API Classification System for Security
HyperAI Super Neural
HyperAI Super Neural
Mar 5, 2026 · Artificial Intelligence

ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)

Using a dataset of 11,647 hepatocellular carcinoma patients, a French research team combined ensemble learning, SHAP explainability, UMAP dimensionality reduction and K‑medoids clustering to build an interpretable model that outperforms traditional scores in predicting three‑month wait‑list mortality and defines seven clinically distinct risk sub‑groups.

Hepatocellular CarcinomaK-MedoidsLiver Transplantation
0 likes · 14 min read
ML Predicts Dual Mortality Risk for HCC Liver Transplant Candidates (11,647 Cases)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 27, 2025 · Artificial Intelligence

IKNet: Explainable Stock Price Forecasting with News Keywords and Technical Indicators

IKNet combines FinBERT‑derived news keywords with technical‑indicator time series, uses SHAP to quantify each feature's impact, and achieves a 32.9% RMSE reduction and 18.5% higher cumulative returns on the S&P 500 (2015‑2024) compared with RNN and Transformer baselines, while providing fine‑grained, context‑aware explanations of price movements.

FinBERTSHAPdeep learning
0 likes · 11 min read
IKNet: Explainable Stock Price Forecasting with News Keywords and Technical Indicators
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 6, 2025 · Artificial Intelligence

Time Series Paper Digest (Aug 23–Sep 5 2025)

It presents concise summaries of six recent arXiv papers on unsupervised domain adaptation, efficient forecasting, SHAP explanations, text‑reinforced multimodal forecasting, online prediction with feature adjustment, zero‑shot forecasting zoo, and a new anomaly‑detection metric, highlighting methods, datasets, and results.

Anomaly DetectionMultimodal LearningOnline Learning
0 likes · 16 min read
Time Series Paper Digest (Aug 23–Sep 5 2025)
Instant Consumer Technology Team
Instant Consumer Technology Team
Jul 2, 2025 · Operations

How to Build a Full‑Chain Metric Anomaly Detection Framework for Business Operations

This article explains how to design a complete metric‑abnormality pipeline—from real‑time threshold alerts and statistical tests such as 3σ, GESD, IQR, and MBP to trend analysis with Mann‑Kendall and Prophet, and finally to deterministic and probabilistic attribution using contribution decomposition and SHAP, all illustrated with practical business cases.

Business AnalyticsProphet modelSHAP
0 likes · 20 min read
How to Build a Full‑Chain Metric Anomaly Detection Framework for Business Operations
DataFunSummit
DataFunSummit
Sep 3, 2024 · Artificial Intelligence

Metric Attribution on Internet Platforms: Concepts, Methods, and Tool Applications

This article explains metric attribution for internet platforms, covering its definition, a three‑step analytical framework, deterministic and probabilistic methods such as metric decomposition, machine‑learning models with SHAP values, case studies, and a practical tool that guides users through attribution analysis.

Internet PlatformsMachine LearningSHAP
0 likes · 15 min read
Metric Attribution on Internet Platforms: Concepts, Methods, and Tool Applications
DataFunTalk
DataFunTalk
Jul 13, 2024 · Artificial Intelligence

Metric Attribution in Internet Platforms: Concepts, Methods, and Case Studies

This article explains metric attribution for internet platforms, covering its definition, a three‑step framework, basic deterministic and probabilistic methods—including indicator decomposition, machine‑learning and SHAP techniques—illustrated with two detailed case studies and a brief overview of supporting tools.

Data AnalysisInternet PlatformsSHAP
0 likes · 15 min read
Metric Attribution in Internet Platforms: Concepts, Methods, and Case Studies
Model Perspective
Model Perspective
Oct 31, 2022 · Artificial Intelligence

Understanding SHAP: How Shapley Values Explain Black‑Box Models

This article explains the SHAP (Shapley Additive Explanation) method, its theoretical foundations in game theory, the computation of Shapley Values, various algorithmic approximations like TreeSHAP and DeepSHAP, practical code examples, and the strengths and limitations of using SHAP for model interpretability.

Model InterpretationSHAPShapley Values
0 likes · 11 min read
Understanding SHAP: How Shapley Values Explain Black‑Box Models
DataFunTalk
DataFunTalk
Sep 17, 2021 · Artificial Intelligence

Interpretable Machine Learning: Methods, Tools, and Financial Applications

This article introduces the importance of model interpretability, reviews common explanation techniques such as model‑specific and model‑agnostic methods, global and local analyses, partial dependence plots, ICE, ALE, and tools like LIME and SHAP, and demonstrates their practical use in anti‑fraud and device‑classification scenarios within a financial‑technology context.

LIMEMachine LearningSHAP
0 likes · 14 min read
Interpretable Machine Learning: Methods, Tools, and Financial Applications
G7 EasyFlow Tech Circle
G7 EasyFlow Tech Circle
Sep 15, 2021 · Artificial Intelligence

Can Predictive Models Uncover Causal Effects? A Truck Risk Case Study

Using a road freight accident prediction example, the article warns that interpreting predictive model explanations as causal effects can be misleading, explains when such models may answer causal questions, demonstrates SHAP analysis on an XGBoost model, and recommends causal inference tools like ecoml for reliable effect estimation.

Machine LearningRisk PredictionSHAP
0 likes · 10 min read
Can Predictive Models Uncover Causal Effects? A Truck Risk Case Study
DataFunTalk
DataFunTalk
Jan 3, 2020 · Artificial Intelligence

Survey of Machine Learning Model Interpretability Techniques

This article provides a comprehensive survey of model interpretability in machine learning, covering its importance, evaluation criteria, and a wide range of techniques such as permutation importance, partial dependence plots, ICE, LIME, SHAP, RETAIN, and LRP, along with practical code examples and visualizations.

ICELIMEMachine Learning
0 likes · 39 min read
Survey of Machine Learning Model Interpretability Techniques
Didi Tech
Didi Tech
Oct 8, 2019 · Artificial Intelligence

Didi and Ant Financial Co‑Develop SQLFlow to Bring AI Capabilities to Data Analysts

Partnering with Ant Financial, Didi enhanced the open-source SQLFlow platform—translating SQL into end-to-end AI workflows with added deep-learning, XGBoost, clustering and SHAP explanation capabilities and Hive support—to create a “SQL garden” marketplace where analysts can deploy ready-made AI models via simple SQL, speeding enterprise AI adoption.

AIOpen SourceSHAP
0 likes · 9 min read
Didi and Ant Financial Co‑Develop SQLFlow to Bring AI Capabilities to Data Analysts