Algorithm Testing Practices and Machine Learning Foundations at Hello

This document shares the algorithm testing team's experiences and knowledge accumulated during daily testing work, organized into several sections.

Machine Learning Basics : Introduces the three main categories of machine learning—supervised learning, unsupervised learning, and deep learning. Supervised learning maps inputs to labeled outputs and includes classification (categorical targets) and regression (continuous targets). Unsupervised learning works with unlabeled data, covering clustering and density estimation. Deep learning is described as a multi‑layer nonlinear transformation approach that automatically extracts features.

Hello Algorithm Application Scenarios : Lists practical use cases across the company, such as intelligent vehicle dispatch, pricing, location services, asset protection, driver‑passenger matching, computer vision, risk control, natural language processing, and data science recommendations.

Key Terminology : Defines features (e.g., weight, wingspan), target variables (categorical vs. continuous), training samples (features combined into instances), knowledge representation (rules, probability distributions, or exemplar instances), classification, regression, and clustering.

Algorithm Development Steps : (1) Data collection, (2) Input preparation, (3) Data analysis and cleaning, (4) Model training, (5) Model testing and evaluation, (6) Deployment for prediction.

Four Dimensions of Algorithm Testing Capability :

Data Quality Assurance – building a data‑comparison platform to monitor business data quality, detecting over 25 issues by June.

Dependency Service Quality Assurance – creating a service availability probing platform (e.g., for ASR) that reduced issue resolution time from 2.5 days to 30 minutes.

Model Performance and Effectiveness Assurance – providing self‑service model performance testing on the AI platform and establishing supervised‑learning model effect evaluation pipelines (data preprocessing, model data service, effect evaluation).

System Engineering Quality Assurance – aligning algorithm testing system quality with overall business quality standards.

Basic Capability Support – maintaining foundational infrastructure consistent with other business lines.

The document concludes by thanking readers and encouraging continued collaboration to improve software quality at Hello.