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ML 5 — Evaluating Machine Learning Models: How to Measure Success 📊
How to Evaluate Your ML Model: A Guide to Offline and Online Methods
After you’ve trained your machine learning model, the next essential step is evaluation. You need to understand how well your model is performing to make adjustments and improvements. Whether you’re testing it offline using historical data or doing real-time online evaluation, measuring the performance of your model is crucial to ensuring it works in the real world. In this article, we’ll explore both offline and online evaluation techniques, how they differ, and how to choose the right one for your use case.
1. Offline Evaluation: Testing with Historical Data ⏳
Offline evaluation is the traditional method of assessing the performance of a machine learning model. In this method, the model is tested on a dataset that is already available and was not used during the training phase. Think of it like a final exam for your model — you’re using past data to understand how well the model performs. Here’s a breakdown:
Why Offline Evaluation?
Offline evaluation is crucial because it gives you an insight into how well the model…
