Estimating the overall user experience (UX) on a device is a common challenge
faced by manufacturers. Today, device makers primarily rely on microbenchmark
scores, such as Geekbench, that stress test specific hardware components, such
as CPU or RAM, but do not satisfactorily capture consumer workloads. System
designers often rely on domain-specific heuristics and extensive testing of
prototypes to reach a desired UX goal, and yet there is often a mismatch
between the manufacturers' performance claims and the consumers' experience.
We present our initial results on predicting real-life experience on laptops
from their hardware specifications. We target web applications that run on
Chromebooks (ChromeOS laptops) for a simple and fair aggregation of experience
across applications and workloads. On 54 laptops, we track 9 UX metrics on
common end-user workloads: web browsing, video playback and audio/video calls.
We focus on a subset of high-level metrics exposed by the Chrome browser, that
are part of the Web Vitals initiative for judging the UX on web applications.
With a dataset of 100K UX data points, we train gradient boosted regression
trees that predict the metric values from device specifications. Across our 9
metrics, we note a mean $R^2$ score (goodness-of-fit on our dataset) of 97.8%
and a mean MAAPE (percentage error in prediction on unseen data) of 10.1%.

通过硬件规格预测设备用户体验在实际应用中的表现，对于厂商来说是一个常见的挑战。使用了梯度提升回归树模型，通过设备规格预测了在常见用户工作负载下的用户体验指标，并在数据集上达到了 97.8% 的拟合优度和 10.1% 的预测误差。

从硬件规格预测笔记本电脑用户体验

Predicting User Experience on Laptops from Hardware Specifications

This report provides a concise overview of the proposed North system, which
aims to achieve automatic word/syllable recognition for Taiwanese Hakka
(Sixian). The report outlines three key components of the system: the
acquisition, composition, and utilization of the training data; the
architecture of the model; and the hardware specifications and operational
statistics. The demonstration of the system can be found at
this https URL

该报告提供了对北方系统的简要概述，旨在实现台湾客家语（四县）的自动词 / 音节识别。报告概述了系统的三个关键部分：训练数据的获取、组成和利用；模型的架构；硬件规格和操作统计。系统的演示可在此 https URL 中找到。