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DataFunSummit
DataFunSummit
May 31, 2023 · Artificial Intelligence

Evolution of Face Detection Techniques: Datasets, Research Directions, and Future Work

This article reviews the evolution of face detection, covering the Widely‑Face dataset, major research directions such as feature fusion, label assignment, auxiliary supervision, anchor‑free methods, NAS‑based designs, summarizes key papers from S3FD to MogFace, introduces ModelScope implementations, and outlines future challenges and opportunities.

AI researchFace Detectioncomputer vision
0 likes · 13 min read
Evolution of Face Detection Techniques: Datasets, Research Directions, and Future Work
GuanYuan Data Tech Team
GuanYuan Data Tech Team
May 25, 2023 · Artificial Intelligence

How to Build a Comprehensive ML Model Quality Assessment Framework

This article explains why and how to evaluate machine learning model quality through a structured framework that covers data validation, feature checks, and algorithm testing, helping ensure accuracy, reliability, and maintainability before deployment.

AI governancedata validationfeature engineering
0 likes · 19 min read
How to Build a Comprehensive ML Model Quality Assessment Framework
Full-Stack Trendsetter
Full-Stack Trendsetter
May 18, 2023 · Artificial Intelligence

How 360 and ChatGLM Are Building China’s “Microsoft + OpenAI” Large‑Model Duo

On May 16, 360 and Zhipu AI announced a strategic partnership to co‑develop the trillion‑parameter models 360GLM and 360GPT, positioning them as China’s answer to Microsoft‑OpenAI by combining large‑scale pre‑training, bilingual capabilities, and integration with 360’s search and browser ecosystem.

360AI CollaborationChatGLM
0 likes · 7 min read
How 360 and ChatGLM Are Building China’s “Microsoft + OpenAI” Large‑Model Duo
DataFunTalk
DataFunTalk
Feb 21, 2023 · Artificial Intelligence

Analysis of Large Language Models: Capabilities, Training Methods, and Limitations – Summary of Prof. Qiu Xipeng’s Lecture

Prof. Qiu Xipeng’s lecture provides a comprehensive overview of large language models—from their historical development and architectural foundations to key technologies such as in‑context learning, chain‑of‑thought, and natural‑instruction learning, as well as RLHF training, capability evaluation, and current limitations of ChatGPT.

Artificial IntelligenceChatGPTIn-Context Learning
0 likes · 15 min read
Analysis of Large Language Models: Capabilities, Training Methods, and Limitations – Summary of Prof. Qiu Xipeng’s Lecture
Python Programming Learning Circle
Python Programming Learning Circle
Dec 7, 2022 · Artificial Intelligence

Predicting the 2022 FIFA World Cup Champion Using Machine Learning Models

This article details a data‑mining project that uses historical World Cup match data, extensive feature engineering, and various machine‑learning algorithms—including neural networks, logistic regression, SVM, decision trees, and random forests—to predict the champion of the 2022 tournament, while analyzing model errors and proposing improvements.

World Cupclassificationdata preprocessing
0 likes · 7 min read
Predicting the 2022 FIFA World Cup Champion Using Machine Learning Models
Laiye Technology Team
Laiye Technology Team
Nov 23, 2022 · Artificial Intelligence

Design and Practices of a Data‑Driven OCR Testing System

The article describes Laiye's shift to a data‑driven deep‑learning workflow and presents the design, macro‑ and micro‑analysis features, visual diff tools, distributed tracing, and code examples of their OCR testing system that accelerate model evaluation and iterative optimization.

AIData‑DrivenMLOps
0 likes · 11 min read
Design and Practices of a Data‑Driven OCR Testing System
HaoDF Tech Team
HaoDF Tech Team
Oct 8, 2022 · Artificial Intelligence

Exploring Transformer Technology and Its Applications in NLP, Computer Vision, and OCR at Haodf.com

This article introduces the Transformer architecture, explains its attention mechanism, details its adaptations for natural language processing, computer vision, and OCR tasks, and presents experimental results of various models such as BERT, ELECTRA, Swin Transformer, and CRNN-BCN on large-scale medical data from Haodf.com.

NLPOCRSwin Transformer
0 likes · 39 min read
Exploring Transformer Technology and Its Applications in NLP, Computer Vision, and OCR at Haodf.com
Model Perspective
Model Perspective
Aug 25, 2022 · Artificial Intelligence

Mastering Regression: Key Assumptions, Metrics, and Model Evaluation

This article explains the fundamental assumptions of linear regression, compares linear and nonlinear models, discusses multicollinearity, outliers, regularization, heteroscedasticity, VIF, stepwise regression, and reviews essential evaluation metrics such as MAE, MSE, RMSE, R² and Adjusted R².

Machine LearningMetricslinear regression
0 likes · 12 min read
Mastering Regression: Key Assumptions, Metrics, and Model Evaluation
Model Perspective
Model Perspective
Jun 17, 2022 · Artificial Intelligence

What Is Classification in Data Mining? Types, Models, and Key Applications

The article explains classification as a data‑analysis task that builds models to assign new observations to predefined categories, outlines its implementation steps, describes various data types (boolean, nominal, ordinal, continuous, discrete), presents common machine‑learning classifiers such as decision trees and neural networks, and highlights practical applications like crime detection, disease risk prediction, and credit assessment.

Machine Learningclassificationdata mining
0 likes · 5 min read
What Is Classification in Data Mining? Types, Models, and Key Applications
DaTaobao Tech
DaTaobao Tech
May 13, 2022 · Big Data

Taobao Big Data Model Governance and DataWorks Co‑development

Taobao’s rapidly expanding technical data system faced naming inconsistencies, low table reuse, and costly, inefficient data usage, prompting a joint effort with DataWorks to digitize model evaluation, enforce standardized governance, deliver intelligent end‑to‑end modeling tools, and launch a development assistant, resulting in a health‑monitoring dashboard, upgraded data maps, and a roadmap for further automation and architecture refinement.

Big DataData GovernanceData Platform
0 likes · 12 min read
Taobao Big Data Model Governance and DataWorks Co‑development
Python Programming Learning Circle
Python Programming Learning Circle
Dec 21, 2021 · Artificial Intelligence

Introduction to CatBoost: Features, Advantages, and Practical Implementation

This article introduces CatBoost, outlines its key advantages such as automatic handling of categorical features, symmetric trees, and feature combination, and provides a step‑by‑step Python tutorial—including data preparation, model training, visualization, and feature importance analysis—using a CTR prediction dataset.

CatBoostMachine LearningPython
0 likes · 5 min read
Introduction to CatBoost: Features, Advantages, and Practical Implementation
DataFunTalk
DataFunTalk
Sep 27, 2021 · Artificial Intelligence

Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation

This article introduces the fundamentals of transfer learning, explains its theoretical foundations and formulas, and demonstrates how multi‑task learning and domain‑adaptation techniques are applied to financial risk‑control scenarios to overcome label scarcity, distribution shift, and model complexity challenges, presenting detailed experimental results and analysis.

deep learningdomain adaptationfinancial risk
0 likes · 17 min read
Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation
HelloTech
HelloTech
Aug 27, 2021 · Artificial Intelligence

Algorithm Testing Practices and Machine Learning Foundations at Hello

The Hello algorithm testing team outlines its workflow—from data collection and cleaning through model training, evaluation, and deployment—while teaching machine‑learning fundamentals, detailing company‑wide use cases, defining key terms, and describing four testing capability dimensions covering data quality, service reliability, model performance, and system engineering.

AISoftware qualityalgorithm testing
0 likes · 12 min read
Algorithm Testing Practices and Machine Learning Foundations at Hello
DataFunTalk
DataFunTalk
Jul 6, 2021 · Artificial Intelligence

Automated End-to-End Model Iteration in Intelligent Risk Control Systems

This article explains how an intelligent risk control system can achieve fully automated, end-to-end model iteration, detailing the multi-layer architecture, sample and feature selection, automated training, evaluation, scoring, deployment, and the efficiency gains compared with manual processes.

AIfeature engineeringmodel automation
0 likes · 20 min read
Automated End-to-End Model Iteration in Intelligent Risk Control Systems
DataFunTalk
DataFunTalk
May 17, 2021 · Artificial Intelligence

Comprehensive Overview of Machine Learning Model Evaluation Metrics

This article provides a comprehensive summary of machine learning model evaluation metrics, covering accuracy, precision, recall, F1, RMSE, ROC/AUC, KS test, and scoring cards, with explanations, formulas, code examples, and practical considerations for model performance assessment.

AUCKSMachine Learning
0 likes · 19 min read
Comprehensive Overview of Machine Learning Model Evaluation Metrics
Python Programming Learning Circle
Python Programming Learning Circle
May 8, 2021 · Artificial Intelligence

Top 10 New Features in Scikit‑learn 0.24

The article reviews the most important additions in scikit‑learn 0.24, including faster hyper‑parameter search methods, ICE plots, histogram‑based boosting improvements, new feature‑selection tools, polynomial‑feature approximations, a semi‑supervised classifier, MAPE metric, enhanced OneHotEncoder and OrdinalEncoder handling, and a more flexible RFE interface.

Machine LearningPythondata preprocessing
0 likes · 8 min read
Top 10 New Features in Scikit‑learn 0.24
DataFunTalk
DataFunTalk
May 1, 2021 · Artificial Intelligence

How to Evaluate Machine Learning Model Performance Before Production Deployment

This tutorial walks through a practical case of predicting employee attrition, demonstrating how to assess and compare machine‑learning models using ROC AUC, confusion matrices, precision‑recall trade‑offs, and the Evidently library to generate performance dashboards, helping choose the best model for production.

HR attritionMachine LearningROC AUC
0 likes · 17 min read
How to Evaluate Machine Learning Model Performance Before Production Deployment
Didi Tech
Didi Tech
Apr 26, 2021 · Artificial Intelligence

Model Quality Assurance Practices at DiDi: Challenges, Solutions, and Evaluation

DiDi’s shift to machine‑learning‑driven ride‑hailing services revealed major QA challenges—data and feature quality, model verification, and API stability—prompting a four‑pillar framework and a unified “Strategy‑Center 1.0” platform to systematically monitor, evaluate, and improve model effectiveness, bias paths, and feature discovery.

AI OperationsFeature EvaluationModel Quality Assurance
0 likes · 8 min read
Model Quality Assurance Practices at DiDi: Challenges, Solutions, and Evaluation
Sohu Tech Products
Sohu Tech Products
Apr 14, 2021 · Artificial Intelligence

Evaluating Machine Learning Model Performance Before Production: An Employee Attrition Case Study

This tutorial walks through a complete workflow for assessing machine‑learning models—using a Kaggle HR attrition dataset, comparing Random Forest and Gradient Boosting via ROC‑AUC, precision, recall and segment analysis with the Evidently library—to decide which model is ready for production deployment.

ROC AUCemployee attritionevidently
0 likes · 17 min read
Evaluating Machine Learning Model Performance Before Production: An Employee Attrition Case Study
58 Tech
58 Tech
Dec 25, 2020 · Artificial Intelligence

User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model

This article presents a comprehensive study on C‑side user identity recognition for internet platforms, addressing cold‑start and sample‑scarcity challenges by comparing keyword matching, XGBoost, TextCNN, a fusion model, and an improved Wide & Deep architecture, showing that the latter achieves the highest F1 score of 80.67%.

TextCNNUser IdentityWide&Deep
0 likes · 13 min read
User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model
JD Retail Technology
JD Retail Technology
Sep 28, 2020 · Artificial Intelligence

Why AI Testing Is Still Painful and How to Solve It

The talk explores the current pain points of AI testing, outlines data‑quality analysis methods, highlights critical ETL and model‑testing considerations, and shares practical case studies and platform designs to improve machine‑learning quality assurance.

AI testingETLMachine Learning
0 likes · 5 min read
Why AI Testing Is Still Painful and How to Solve It
DataFunTalk
DataFunTalk
Sep 26, 2020 · Artificial Intelligence

What Makes a Good Model? Understanding Model Concepts, Types, and Evaluation in Data Science

This article explores the definition of a model, distinguishes business, data, and function models, discusses criteria for a good model—including performance, fidelity to real‑world relationships, and interpretability—and examines why a universal model does not exist, all within the context of data science and AI.

AIInterpretabilityMachine Learning
0 likes · 18 min read
What Makes a Good Model? Understanding Model Concepts, Types, and Evaluation in Data Science
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 28, 2020 · Artificial Intelligence

Avoid Common Pitfalls in Industrial Text Classification: A Practical Guide

This comprehensive guide examines real‑world text classification projects, covering label taxonomy design, data scarcity solutions, efficient annotation, new‑class discovery, algorithm selection, evaluation metrics, OOV handling, model evolution, rule‑model integration, performance‑boosting tricks, and inference under resource constraints.

NLPadversarial validationalgorithm selection
0 likes · 15 min read
Avoid Common Pitfalls in Industrial Text Classification: A Practical Guide
DataFunTalk
DataFunTalk
Aug 14, 2020 · Artificial Intelligence

Illustrated Guide to the Complete Machine Learning Workflow

This article presents a hand‑drawn, illustrated walkthrough of the entire machine‑learning pipeline—from dataset definition, exploratory data analysis, preprocessing, and data splitting to model building, algorithm selection, hyper‑parameter tuning, feature selection, and evaluation for both classification and regression tasks.

Machine Learningclassificationcross-validation
0 likes · 17 min read
Illustrated Guide to the Complete Machine Learning Workflow
DataFunTalk
DataFunTalk
Apr 12, 2020 · Artificial Intelligence

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

In this article, Wang Zhe addresses fifteen common questions about recommendation systems, covering topics such as building cross‑domain knowledge, the role of deep reinforcement learning, handling sparse or low‑sample data, offline‑online evaluation, knowledge graphs, graph neural networks, model interpretability, large‑scale ID embedding, and career advice for engineers.

Graph Neural NetworkKnowledge GraphRecommendation Systems
0 likes · 14 min read
Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems
37 Interactive Technology Team
37 Interactive Technology Team
Apr 28, 2019 · Artificial Intelligence

Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring

By applying XGBoost‑based regression models that are retrained daily on two‑week order data and tuned per sub‑package, the mobile‑game recharge monitoring system predicts 10‑minute order volumes, sharply cuts false alarms from hundreds to dozens, and delivers precise, scalable alerts for game operations.

Anomaly DetectionMobile GamingXGBoost
0 likes · 8 min read
Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 6, 2019 · Artificial Intelligence

How Deep Learning Unwarps Curved Document Images for Better OCR

This article explores how deep‑learning‑based image dewarping techniques, from traditional hardware methods to modern U‑Net, Stacked U‑Net and Dilated U‑Net architectures, can correct warped document photos, improve OCR accuracy, and support intelligent verification in high‑throughput business scenarios.

OCRU‑Netdeep learning
0 likes · 19 min read
How Deep Learning Unwarps Curved Document Images for Better OCR
HomeTech
HomeTech
Feb 28, 2019 · Artificial Intelligence

How to Systematically Test and Monitor AI Models in Large‑Scale Production

This article presents a comprehensive approach to testing, automating, and monitoring AI prediction models in a high‑traffic environment, covering background, challenges, evaluation metrics, data sampling methods, automated test scripts, and online monitoring to ensure model accuracy, performance, and reliability.

AI testingAutomationBig Data
0 likes · 13 min read
How to Systematically Test and Monitor AI Models in Large‑Scale Production
JD Tech Talk
JD Tech Talk
Jan 16, 2019 · Artificial Intelligence

Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results

This article presents a detailed case study of building a purchase‑user prediction model by integrating Convolutional Neural Networks for feature extraction with Long Short‑Term Memory networks for time‑series forecasting, covering background, model structure, data augmentation, experimental results, and business impact.

CNNLSTMTime-series
0 likes · 10 min read
Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results
Qunar Tech Salon
Qunar Tech Salon
Sep 18, 2018 · Artificial Intelligence

Scikit-learn Tutorial: Supervised Learning with Linear Regression

This article provides a comprehensive guide to using Python's scikit-learn library for supervised learning, focusing on linear regression, covering theoretical background, environment setup, data preprocessing, model training, evaluation with mean squared error, cross‑validation, and detailed code examples.

Pythoncross-validationlinear regression
0 likes · 14 min read
Scikit-learn Tutorial: Supervised Learning with Linear Regression
Architecture Digest
Architecture Digest
Jul 12, 2018 · Artificial Intelligence

How to Choose the Right Machine Learning Algorithm

This article explains that there is no universal solution for selecting machine learning algorithms and outlines practical factors—such as data characteristics, problem type, business constraints, and algorithm complexity—to help practitioners systematically narrow down and pick the most suitable models.

Machine Learningalgorithm selectiondata preprocessing
0 likes · 14 min read
How to Choose the Right Machine Learning Algorithm
Tencent Cloud Developer
Tencent Cloud Developer
Jun 25, 2018 · Artificial Intelligence

Using MLP for Image Classification: Implementation, Results, and Limitations

The article demonstrates how a simple fully‑connected MLP can be trained on a small 64×64×3 cat‑vs‑non‑cat dataset, achieving perfect training accuracy but only 78 % test accuracy, and explains that parameter explosion, vanishing gradients, and lack of spatial invariance limit MLPs, motivating the shift to CNNs.

H5pyImage ClassificationMLP
0 likes · 15 min read
Using MLP for Image Classification: Implementation, Results, and Limitations
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 22, 2018 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This beginner-friendly guide walks through core machine‑learning concepts—from data organization and feature design to supervised and unsupervised algorithms such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques—while explaining model evaluation, overfitting, and practical tuning strategies.

Machine LearningUnsupervised Learningdeep learning
0 likes · 8 min read
Essential Machine Learning Algorithms Every Beginner Must Know
Baidu Intelligent Testing
Baidu Intelligent Testing
May 8, 2018 · Artificial Intelligence

Interview with Baidu QATC Chair Yang Fei on AI Testing Challenges and the Future of QA

In this interview, Baidu QATC chair Yang Fei discusses his career, the evolving scope of quality assurance from code to AI model testing, key challenges such as service quality and model interpretability, practical approaches for defect discovery, continuous evaluation pipelines, and advice for QA professionals' personal growth.

AIBaiduQA
0 likes · 7 min read
Interview with Baidu QATC Chair Yang Fei on AI Testing Challenges and the Future of QA
Architecture Digest
Architecture Digest
Feb 14, 2018 · Artificial Intelligence

Comparative Analysis and Optimization of Machine Learning Models on the UCI Census Income Dataset

This article walks through a complete machine‑learning workflow on the UCI Census Income dataset, covering data exploration, preprocessing (including log‑transformation and scaling), model training with Naïve Bayes, Decision Tree and SVM, performance evaluation, hyper‑parameter tuning via grid search, feature importance analysis, and feature selection, providing code snippets and visualizations.

Machine LearningPythondata preprocessing
0 likes · 24 min read
Comparative Analysis and Optimization of Machine Learning Models on the UCI Census Income Dataset
Hulu Beijing
Hulu Beijing
Jan 18, 2018 · Artificial Intelligence

Why Accuracy Misleads and How to Pick Better ML Evaluation Metrics

This article uses realistic Hulu business scenarios to illustrate the pitfalls of relying solely on accuracy, precision, recall, RMSE, and other single metrics, and explains how combining complementary evaluation measures such as average accuracy, precision‑recall curves, ROC, F1‑score, and MAPE can provide a more comprehensive assessment of classification, ranking, and regression models.

RMSEaccuracyfeature engineering
0 likes · 12 min read
Why Accuracy Misleads and How to Pick Better ML Evaluation Metrics
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Dec 5, 2017 · Artificial Intelligence

10 Must‑Know Machine Learning Algorithms for Engineers

From foundational concepts to practical examples, this guide walks engineers through ten essential supervised and unsupervised machine‑learning algorithms—decision trees, Naïve Bayes, linear regression, logistic regression, SVM, ensemble methods, clustering, PCA, SVD, and ICA—explaining their theory, real‑world uses, and why they matter.

AlgorithmsArtificial IntelligenceMachine Learning
0 likes · 11 min read
10 Must‑Know Machine Learning Algorithms for Engineers
Hulu Beijing
Hulu Beijing
Nov 9, 2017 · Artificial Intelligence

Mastering ROC Curves: How to Plot and Compute AUC for Binary Classification

This article explains the fundamentals of ROC curve construction, the calculation of AUC, compares ROC with PR curves, and provides step‑by‑step examples—including a medical diagnosis scenario and threshold adjustments—to help readers accurately evaluate binary classification models.

AUCROCbinary classification
0 likes · 10 min read
Mastering ROC Curves: How to Plot and Compute AUC for Binary Classification
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 28, 2017 · Fundamentals

The Evolution of Software Engineering and the Role of Development Models

The article traces the history of software engineering as a continual shortening of release cycles, discusses the persistent emergence of new development models—from the waterfall to spiral and iterative approaches—and highlights George Box’s insight that while all models are imperfect, the useful ones illuminate practice.

Methodologydevelopment modelsmodel evaluation
0 likes · 3 min read
The Evolution of Software Engineering and the Role of Development Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 27, 2017 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This guide introduces beginners to core machine learning concepts, covering feature design, supervised and unsupervised methods such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques like bagging and boosting, while explaining model evaluation, overfitting, and practical optimization strategies.

Machine LearningUnsupervised Learningensemble methods
0 likes · 9 min read
Essential Machine Learning Algorithms Every Beginner Must Know
Architects' Tech Alliance
Architects' Tech Alliance
Nov 24, 2016 · Big Data

Data Mining Overview: Process, Techniques, and Model Evaluation

This article provides a comprehensive introduction to data mining, covering its definition, goal setting, data sampling, exploration, preprocessing, pattern discovery, model building, evaluation methods, and the main analytical techniques such as classification, regression, clustering, association rules, feature and deviation analysis, and web mining.

Machine Learningassociation rulesclassification
0 likes · 10 min read
Data Mining Overview: Process, Techniques, and Model Evaluation
21CTO
21CTO
Sep 20, 2016 · Artificial Intelligence

What Quora’s VP Reveals About Building Real‑World Recommender Systems

In this talk, Quora’s VP of Engineering Xavier Amatriain shares practical lessons from building the company’s large‑scale recommender system, covering data richness, implicit signals, model choices, feature engineering, evaluation strategies, and why distribution isn’t always required.

feature engineeringimplicit feedbackmatrix factorization
0 likes · 4 min read
What Quora’s VP Reveals About Building Real‑World Recommender Systems
21CTO
21CTO
Sep 14, 2015 · Artificial Intelligence

How Airbnb Builds Machine Learning Models to Detect Fraudulent Transactions

Airbnb’s trust and safety team uses a series of machine‑learning models—starting from defining the prediction target, through data sampling and feature engineering, to evaluating precision and recall—to identify and mitigate fraud risks such as chargebacks across its global peer‑to‑peer rental platform.

AIAirbnbfeature engineering
0 likes · 7 min read
How Airbnb Builds Machine Learning Models to Detect Fraudulent Transactions
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Aug 2, 2015 · Artificial Intelligence

Designing Machine Learning Models for Fraud Detection: Sampling, Feature Engineering, and Evaluation

This article explains how Airbnb's Trust & Safety team builds machine‑learning models to detect fraudulent behavior, covering problem definition, role‑based sampling, feature design techniques such as normalization and CP‑coding, and the trade‑offs between precision and recall in model evaluation.

AISamplingfeature engineering
0 likes · 10 min read
Designing Machine Learning Models for Fraud Detection: Sampling, Feature Engineering, and Evaluation