--- title: What Training Five Machine Learning Models On The Same... description: A beginner ML practitioner shares lessons learned from training five classification models (Logistic Regression, Gaussian Naive Bayes, Decision Tree, Random... canonical: https://daily.dev/posts/what-training-five-machine-learning-models-on-the-same-dataset-taught-me-about-machine-learning--atcb6m9em twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: What Training Five Machine Learning Models On The Same Dataset Taught Me About Machine Learning. | daily.dev og:description: A beginner ML practitioner shares lessons learned from training five classification models (Logistic Regression, Gaussian Naive Bayes, Decision Tree, Random... og:url: https://daily.dev/posts/what-training-five-machine-learning-models-on-the-same-dataset-taught-me-about-machine-learning--atcb6m9em og:image: https://api.daily.dev/og/posts/ATCB6M9eM.png og:image:alt: What Training Five Machine Learning Models On The Same Dataset Taught Me About Machine Learning. og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # What Training Five Machine Learning Models On The Same Dataset Taught Me About Machine Learning. **[Medium](https://daily.dev/sources/medium_js)** · 5 min read · 2 upvotes · 0 comments ## Summary A beginner ML practitioner shares lessons learned from training five classification models (Logistic Regression, Gaussian Naive Bayes, Decision Tree, Random Forest, KNN) on the same diabetes dataset. The key insight is that keeping the dataset constant while changing only the algorithm reveals how each model thinks and behaves. The author also challenges the assumption that accuracy is the only metric that matters, highlighting precision, recall, F1-score, and ROC-AUC as essential for choosing the right model for a given problem. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why did my model with the highest accuracy still perform poorly on recall for a healthcare dataset? High accuracy does not guarantee a model identifies most positive cases; a classifier can score well overall while missing many diseased patients. In one comparison across five models on a diabetes dataset, Gaussian Naive Bayes had the best accuracy and ROC-AUC, but the decision tree achieved the highest recall, showing accuracy alone is misleading for imbalanced or high-stakes prediction tasks. _daily.dev surfaces practical model-evaluation lessons for developers comparing precision, recall, and accuracy trade-offs._ ### Which classification algorithm gives the best precision after hyperparameter tuning on a diabetes dataset? K-Nearest Neighbors achieved the highest precision after hyperparameter tuning when compared against logistic regression, Gaussian Naive Bayes, decision tree, and random forest on the same diabetes dataset. Meanwhile Gaussian Naive Bayes led on accuracy and ROC-AUC, and the decision tree led on recall, illustrating that different algorithms optimize different metrics. _Developers weighing precision versus recall trade-offs can track model comparison write-ups like this on daily.dev._ ## Similar posts on daily.dev - [Crack Machine Learning : 7 Essential Algorithms Made Easy](https://daily.dev/posts/crack-machine-learning-7-essential-algorithms-made-easy-gzumtt40w) · Medium · 0 upvotes · 0 comments - [My Data Science & AI/ML Learning Journey — Part 10](https://daily.dev/posts/my-data-science-ai-ml-learning-journey-part-10-jxbsjhu2p) · Medium · 0 upvotes · 0 comments - [A Visual Introduction to Machine Learning](https://daily.dev/posts/a-visual-introduction-to-machine-learning-dnkoizyrs) · Hacker News · 0 upvotes · 0 comments - [Building Smart Machine Learning in Low-Resource Settings](https://daily.dev/posts/building-smart-machine-learning-in-low-resource-settings-c2lqa3ih7) · Machine Learning Mastery · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#data-science](https://daily.dev/tags/data-science), [#classification](https://daily.dev/tags/classification), [#scikit](https://daily.dev/tags/scikit) [View this post on daily.dev](https://daily.dev/posts/what-training-five-machine-learning-models-on-the-same-dataset-taught-me-about-machine-learning--atcb6m9em) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"What Training Five Machine Learning Models On The Same Dataset Taught Me About Machine Learning.","url":"https://daily.dev/posts/what-training-five-machine-learning-models-on-the-same-dataset-taught-me-about-machine-learning--atcb6m9em","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/what-training-five-machine-learning-models-on-the-same-dataset-taught-me-about-machine-learning--atcb6m9em"},"datePublished":"2026-07-26T01:16:12.039Z","dateModified":"2026-09-13T19:41:27.886Z","description":"A beginner ML practitioner shares lessons learned from training five classification models (Logistic Regression, Gaussian Naive Bayes, Decision Tree, Random...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/97f6a48980273cc3b918b83e448d7b07?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/97f6a48980273cc3b918b83e448d7b07?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/what-training-five-machine-learning-models-on-the-same-dataset-taught-me-about-machine-learning--atcb6m9em","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":2},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,python,data-science,classification,scikit","timeRequired":"PT5M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"What Training Five Machine Learning Models On The Same Dataset Taught Me About Machine Learning."}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/what-training-five-machine-learning-models-on-the-same-dataset-taught-me-about-machine-learning--atcb6m9em#faq","mainEntity":[{"@type":"Question","name":"Why did my model with the highest accuracy still perform poorly on recall for a healthcare dataset?","acceptedAnswer":{"@type":"Answer","text":"High accuracy does not guarantee a model identifies most positive cases; a classifier can score well overall while missing many diseased patients. In one comparison across five models on a diabetes dataset, Gaussian Naive Bayes had the best accuracy and ROC-AUC, but the decision tree achieved the highest recall, showing accuracy alone is misleading for imbalanced or high-stakes prediction tasks. daily.dev surfaces practical model-evaluation lessons for developers comparing precision, recall, and accuracy trade-offs."}},{"@type":"Question","name":"Which classification algorithm gives the best precision after hyperparameter tuning on a diabetes dataset?","acceptedAnswer":{"@type":"Answer","text":"K-Nearest Neighbors achieved the highest precision after hyperparameter tuning when compared against logistic regression, Gaussian Naive Bayes, decision tree, and random forest on the same diabetes dataset. Meanwhile Gaussian Naive Bayes led on accuracy and ROC-AUC, and the decision tree led on recall, illustrating that different algorithms optimize different metrics. Developers weighing precision versus recall trade-offs can track model comparison write-ups like this on daily.dev."}}]} ```