This article presents a comprehensive benchmark of 19 large language models (plus a human baseline) on generating analytical SQL queries over a 200 million‑row GitHub events dataset, detailing the methodology, metrics, results, and practical guidance for using LLMs in data analysis.
This guide explains how to construct production‑ready AI agent systems by balancing cost, latency, and accuracy, offering a decision framework, concrete techniques such as planner‑executor architecture, chain‑of‑thought prompting, verification agents, parallel agents, and file‑system state management, plus real‑world examples and impact metrics.
This article provides a concise technical overview of AI chip key metrics—accuracy, throughput, latency, and energy consumption—explains their impact on hardware design, discusses critical design points such as MAC reduction and processing element optimization, and summarizes practical takeaways for evaluating AI accelerator solutions.
This article outlines six essential criteria—accuracy, robustness, simplicity, explainability, generalization, and scalability—for evaluating the quality of mathematical models such as e‑commerce recommendation systems, helping readers assess whether a model is truly reliable or merely a flashy façade.
This tutorial explains how to compute accuracy, precision, recall, F1‑score, and ROC‑AUC from a confusion matrix using sklearn, TensorFlow, and hand‑crafted Python code, illustrating each metric with example data and visualizations.
This article examines ChatGPT's limitations in real‑time information and answer accuracy, then proposes a retrieval‑augmented method that combines up‑to‑date document search with large language models to deliver more reliable and current responses across various scenarios.
The article examines ChatGPT's limitations in timeliness and factual accuracy, especially for security‑related queries, and proposes a method that combines external document search with the model to deliver up‑to‑date, reliable answers across intelligent‑assistant scenarios.
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.
The article explains the importance of data quality in the big data era, defines key quality dimensions such as completeness, uniqueness, validity, consistency, accuracy, timeliness, and credibility, and details how each dimension can be measured and enforced through specific constraints and validation rules.
After a three‑month contest co‑hosted by iQIYI and ACM MM, 255 teams competed on the challenging iQIYI‑VID‑2019 multimodal dataset, and the BUPT Automation School team won with a 91.14% person‑recognition accuracy, advancing the field and enhancing iQIYI’s video recommendation and AI services.
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.
This article explains how to construct regular expressions that balance matching expected strings, avoiding false matches, maintaining readability, and achieving performance, while covering when to use regex, practical JavaScript examples, accuracy techniques for phone numbers and floats, and key optimization strategies.
