{"schema_version":"onlylabs.public_signal.v1","title":"Scaleway Writing: Supervised Machine Learning done right: getting the labels that you want","description":"Scaleway writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/895e2eab-971d-4ed5-83a7-165bef038423","json_url":"https://onlylabs.fyi/signals/895e2eab-971d-4ed5-83a7-165bef038423/signal.json","generated_at":"2026-06-07T21:17:34.413717+00:00","org":{"slug":"scaleway","name":"Scaleway","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/scaleway","dossier_json_url":"https://onlylabs.fyi/labs/scaleway/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/895e2eab-971d-4ed5-83a7-165bef038423","signal_json":"https://onlylabs.fyi/signals/895e2eab-971d-4ed5-83a7-165bef038423/signal.json","source":"https://www.scaleway.com/en/blog/supervised-machine-learning-done-right/","lab_dossier":"https://onlylabs.fyi/labs/scaleway","lab_dossier_json":"https://onlylabs.fyi/labs/scaleway/dossier.json","analysis":"https://onlylabs.fyi/analysis/scaleway","analysis_json":"https://onlylabs.fyi/analysis/scaleway/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/scaleway/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Scaleway published Supervised Machine Learning done right: getting the labels that you want. 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This talking signal gives public context for research themes, product direction, policy, or launch framing. 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Despite having spent most of their classroom time learning various algorithms, newly employed engineers and data scientists soon find that no amount of state-of-the-art models and clever fine-tuning of hyperparameters can make up for the poor quality of their training data. Here we’ll discuss a few of the most common data issues, and the possible solutions to circumvent them. It must be noted that the nature of problems encountered differs when dealing with structured (i.e. tabular) and unstructured (e.g., imaging, video, or textual) data. Of course, in both cases you want the training data to come from the same distribution as the data that the inference will be performed on. Having a balanced dataset (one where the number of examples belonging to different classes does not differ too much) is also desirable, although not always feasible. Fortunately, there are algorithmic approaches that allow us to deal with imbalanced datasets. When it comes to data “quality”..."},"evidence_pages":[{"url":"https://www.scaleway.com/en/blog/supervised-machine-learning-done-right/","final_url":"https://www.scaleway.com/en/blog/supervised-machine-learning-done-right/","title":"Supervised Machine Learning done right: getting the labels that you want","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:17:34.413717+00:00","bytes":161670,"raw_path":"cee4a163328266e0a6debf1c8ab1f1a278eeabe9c43ef70db94ee115efabb765.html","content_hash":"4deb767b921dc089f6f83537fd92bdfa56aaf45bbba8d3907f1e5fc0d4f1ab7f","excerpt_chars":1200,"truncated":true,"excerpt":"Supervised Machine Learning done right: getting the labels that you want Build • Olga Petrova • 10/12/21 • 4 min read “Garbage in, garbage out” is an expression that Machine Learning practitioners are very familiar with. Despite having spent most of their classroom time learning various algorithms, newly employed engineers and data scientists soon find that no amount of state-of-the-art models and clever fine-tuning of hyperparameters can make up for the poor quality of their training data. Here we’ll discuss a few of the most common data issues, and the possible solutions to circumvent them. It must be noted that the nature of problems encountered differs when dealing with structured (i.e. tabular) and unstructured (e.g., imaging, video, or textual) data. Of course, in both cases you want the training data to come from the same distribution as the data that the inference will be performed on. Having a balanced dataset (one where the number of examples belonging to different classes does not differ too much) is also desirable, although not always feasible. Fortunately, there are algorithmic approaches that allow us to deal with imbalanced datasets. 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