Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Filter by Categories
About Article
Analyze Data
Archive
Best Practices
Better Outputs
Blog
Code Optimization
Code Quality
Command Line
Daily tips
Dashboard
Data Analysis & Manipulation
Data Engineer
Data Visualization
DataFrame
Delta Lake
DevOps
DuckDB
Environment Management
Feature Engineer
Git
Jupyter Notebook
LLM
LLM
Machine Learning
Machine Learning
Machine Learning & AI
Manage Data
MLOps
Natural Language Processing
NumPy
Pandas
Polars
PySpark
Python Tips
Python Utilities
Python Utilities
Scrape Data
SQL
Testing
Time Series
Tools
Visualization
Visualization & Reporting
Workflow & Automation
Workflow Automation

Evaluate Your ML Model Performance with Simple Model Comparison

Table of Contents

Evaluate Your ML Model Performance with Simple Model Comparison

How do you check if your ML model is trained properly? One approach is to use a simple model for comparison.

A simple model establishes a minimum performance benchmark for the given task. A model achieving less or a similar score to the simple model indicates a possible problem with the model.

The code above shows how to evaluate a model’s performance using Deepchecks’ simple model comparison.

Link to Deepchecks.

My previous tips on testing.

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top

Work with Khuyen Tran

Work with Khuyen Tran