Machine Learning InAction Series: Practical Applications in Industry

Introduction

This article discusses machine learning's role in solving industrial problems, focusing on Meituan's practical applications. It covers the entire workflow from problem modeling to model optimization, emphasizing real-world implementation.

Key Components

Problem Modeling: Breaking down complex problems into manageable sub-problems

Data Preparation: Ensuring data quality, distribution consistency, and noise reduction

Feature Engineering: Designing high-level and low-level features for different model types

Model Training: Selecting appropriate algorithms (e.g., Logistic Regression, GBDT) and optimization techniques

Model Optimization: Addressing underfitting/overfitting through data/feature/model adjustments

Practical Insights

The series provides actionable guidance for industrial machine learning applications, including data preprocessing strategies, feature selection methods, and model evaluation techniques tailored for large-scale scenarios.