Why Did AutoML Beat My ML.NET Model? The Answer Surprised Me!"

Last week, I shared my journey using ML.NET to build predictive models, highlighting some key challenges and learnings. (Missed it? Check it out https://www.linkedin.com/feed/update/urn:li:activity:7297761239970021376/).

But something interesting happened... 🤔

Despite using the same algorithm (L-BFGS Poisson Regression) in both ML.NET API and AutoML, the AutoML-generated model outperformed my manually implemented one—by a lot.

So, I dug into the AutoML-generated code to uncover what was really happening behind the scenes. Here’s what I found:

While working on a machine learning solution to optimize resource allocation, I implemented L-BFGS Poisson Regression using ML.NET. Initially, my manually implemented model didn’t achieve the expected prediction accuracy.

However, when I tested AutoML-generated models, I noticed a significant difference—better predictions, improved generalization, and optimized hyperparameters.

Curious about this disparity, I analyzed the AutoML-generated code and uncovered key differences in how model evaluation and optimization are handled behind the scenes.

Key Takeaways from My Analysis:

AutoML Does More Than You Think: Unlike the ML.NET API, AutoML optimizes hyperparameters, evaluates multiple models, and fine-tunes configurations, which significantly impacts prediction accuracy.

Flexibility vs. Automation: The ML.NET API gives full control over model selection, hyperparameter tuning, and data preprocessing, but AutoML automates these steps, making it easier to get high-performing models with minimal effort.

Experience Matters: While AutoML gives great results out of the box, understanding the ML.NET API helps you fine-tune models beyond AutoML’s defaults, unlocking even greater performance tailored to your dataset.

Final Thoughts

🔹 AutoML isn’t just a shortcut—it fills in the gaps that most developers wouldn’t optimize manually.

🔹 But knowing how AutoML works under the hood gives you an edge, letting you push ML.NET even further.

🔹 A hybrid approach—leveraging AutoML while refining with the ML.NET API—can deliver highly efficient, production-ready models.

What’s your experience with ML.NET and AutoML? Have you noticed similar differences in model performance? Let’s discuss!

Follow for more ML.NET insights as I continue exploring real-world machine learning applications!

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