https://www.tunedpredictions.com/blog/Recent content in Blog on Tuned PredictionsHugoenSat, 26 Sep 2026 00:00:00 +0000https://www.tunedpredictions.com/blog/retraining-is-a-new-candidate/Sat, 26 Sep 2026 00:00:00 +0000https://www.tunedpredictions.com/blog/retraining-is-a-new-candidate/<p>New outcomes give you more examples to learn from. They do not guarantee that the next model will be better. Treat retraining as another experiment with an explicit review before deployment.</p> <h2 id="check-what-changed-in-the-data">Check what changed in the data</h2> <p>Before evaluating a new candidate, understand the latest examples. Have labels become more consistent? Has the source changed? Are the new cases representative of the questions the model will face?</p> <p>A larger dataset can still contain incomplete outcomes or inputs that reveal the answer. Revisit the task definition and review the prepared examples whenever the source or business process changes.</p>https://www.tunedpredictions.com/blog/start-with-a-prediction-question/Sat, 26 Sep 2026 00:00:00 +0000https://www.tunedpredictions.com/blog/start-with-a-prediction-question/<p>A useful prediction model starts with a question whose answer you can check. “Improve sales” is a goal. “Will this opportunity close within seven days?” is a task with an outcome and a time window.</p> <h2 id="define-the-outcome-first">Define the outcome first</h2> <p>Decide what counts as a positive outcome and when the answer becomes known. If a deal closes after the seven-day window, it is not a positive example for that question. Consistent labels make training and evaluation easier to interpret.</p>https://www.tunedpredictions.com/blog/evaluate-before-you-deploy/Sat, 26 Sep 2026 00:00:00 +0000https://www.tunedpredictions.com/blog/evaluate-before-you-deploy/<p>A completed training run gives you a candidate model. Whether it helps is a separate question. Answer it with examples the model did not train on and criteria that match your intended use.</p> <h2 id="keep-an-evaluation-set-aside">Keep an evaluation set aside</h2> <p>Held-out examples let you inspect performance on cases outside the training set. Check that duplicate or closely related records have not ended up on both sides of the split. For tasks that change over time, consider whether the test reflects the period in which the model will be used.</p>