--- title: "#xgboost articles | Hashnode" description: "Explore 53 articles tagged #xgboost on Hashnode. Tutorials, guides, and how-tos from the community." url: "https://hashnode.com/tag/xgboost" --- Tag # #xgboost 53 posts · 2 followers [Nitin Sharma](https://hashnode.com/@Nsharma) · [Oct 15](https://hashnode.com/posts/building-a-air-quality-forecasting-pipeline-with-a-feature-store/6ad14aaf637a581e11b89da4) ### [Building a Air Quality Forecasting Pipeline with a Feature Store](https://path2ml.com/building-a-air-quality-forecasting-pipeline-with-a-feature-store?utm_source=hashnode&utm_medium=feed) In this blog, we will use a real example to demonstrate how MLOps can be implemented using GitHub Actions. While this example highlights the importance of MLOps, please note that we are not deploying 9 min read · [path2ml.com](https://path2ml.com/building-a-air-quality-forecasting-pipeline-with-a-feature-store?utm_source=hashnode&utm_medium=feed) [Vashni Roy](https://hashnode.com/@vashniroy) · [Oct 14](https://hashnode.com/posts/does-a-more-complex-ml-model-actually-give-better-results/6acf5b94f069b0121b58b0e5) ### [Does a More Complex ML Model Actually Give Better Results?](https://vashniroy.hashnode.dev/does-a-more-complex-ml-model-actually-give-better-results?utm_source=hashnode&utm_medium=feed) I compared Logistic Regression and XGBoost on the same churn dataset. The simpler model performed better, even across 5-fold cross-validation. 3 min read · [vashniroy.hashnode.dev](https://vashniroy.hashnode.dev/does-a-more-complex-ml-model-actually-give-better-results?utm_source=hashnode&utm_medium=feed) [Faith Mwende](https://hashnode.com/@mwendekiindu) · [Oct 8](https://hashnode.com/posts/my-final-fraud-model-day-7/6ac769cdd3ee440fcb5c9d32) ### [My Final Fraud Model (Day 7)](https://beginnerslog.hashnode.dev/my-final-fraud-model-day-7?utm_source=hashnode&utm_medium=feed) Test AUC was 0.9571, lower than validation's 0.9765. Here's why the test number is the honest one, and why threshold mattered more than model choice. 5 min read · [beginnerslog.hashnode.dev](https://beginnerslog.hashnode.dev/my-final-fraud-model-day-7?utm_source=hashnode&utm_medium=feed) [Faith Mwende](https://hashnode.com/@mwendekiindu) · [Oct 4](https://hashnode.com/posts/picking-a-threshold-is-a-business-decision-day-6/6ac4176b75eb84c85c7c3fd1) ### [Picking a Threshold Is a Business Decision (Day 6)](https://beginnerslog.hashnode.dev/picking-a-threshold-is-a-business-decision-day-6?utm_source=hashnode&utm_medium=feed) The chart doesn't decide for you. I had to. 4 min read · [beginnerslog.hashnode.dev](https://beginnerslog.hashnode.dev/picking-a-threshold-is-a-business-decision-day-6?utm_source=hashnode&utm_medium=feed) [Faith Mwende](https://hashnode.com/@mwendekiindu) · [Oct 4](https://hashnode.com/posts/xgboost-caught-more-fraud-but-there-s-a-catch-day-5/6ac22b2afc58d52f4165a7ca) ### [XGBoost Caught More Fraud - But There's a Catch (Day 5)](https://beginnerslog.hashnode.dev/xgboost-caught-more-fraud-but-there-s-a-catch-day-5?utm_source=hashnode&utm_medium=feed) My recall jumped from 59% to 81%. My precision dropped from 79% to 57%. 4 min read · [beginnerslog.hashnode.dev](https://beginnerslog.hashnode.dev/xgboost-caught-more-fraud-but-there-s-a-catch-day-5?utm_source=hashnode&utm_medium=feed) [Devi Sri Ramasamy](https://hashnode.com/@devisrir) · [Sep 12](https://hashnode.com/posts/chargeback-assistant-an-ai-powered-chargeback-risk-system/6aa4f3dff6df0baf4dd87571) ### [Chargeback Assistant: An AI-Powered Chargeback Risk System](https://devisriblogs.hashnode.dev/chargeback-assistant-an-ai-powered-chargeback-risk-system?utm_source=hashnode&utm_medium=feed) Chargeback AI 5 min read · [devisriblogs.hashnode.dev](https://devisriblogs.hashnode.dev/chargeback-assistant-an-ai-powered-chargeback-risk-system?utm_source=hashnode&utm_medium=feed) [Rishikant](https://hashnode.com/@rishiii2) · [Jul 14](https://hashnode.com/posts/space-to-earth-matrix-revolutionizing-renewable-energy-placement-with-ai/6a56a0ed926698f02d153d2f) ### [Space to Earth Matrix: Revolutionizing Renewable Energy Placement with AI](https://rishiii2.hashnode.dev/space-to-earth-matrix-revolutionizing-renewable-energy-placement-with-ai?utm_source=hashnode&utm_medium=feed) A Technical Deep Dive into the IIT Bombay FOSSEE Mapathon Winning Project 5 min read · [rishiii2.hashnode.dev](https://rishiii2.hashnode.dev/space-to-earth-matrix-revolutionizing-renewable-energy-placement-with-ai?utm_source=hashnode&utm_medium=feed) [AKSHAY](https://hashnode.com/@akshayetukuri) · [Jul 4](https://hashnode.com/posts/predicting-customer-churn-before-it-happens-an-end-to-end-ml-project-with-shap-explainability/6a4925fd5d6713cccaed3468) ### [Predicting Customer Churn Before It Happens: An End-to-End ML Project with SHAP Explainability](https://akshayetukurihashnodedev.hashnode.dev/predicting-customer-churn-before-it-happens-an-end-to-end-ml-project-with-shap-explainability?utm_source=hashnode&utm_medium=feed) An end-to-end churn prediction pipeline using XGBoost and SHAP for explainability — deployed on both Streamlit Cloud and HuggingFace Spaces. Covers EDA insights, feature engineering, model comparison, and a real Docker deployment debugging story. 6 min read · [akshayetukurihashnodedev.hashnode.dev](https://akshayetukurihashnodedev.hashnode.dev/predicting-customer-churn-before-it-happens-an-end-to-end-ml-project-with-shap-explainability?utm_source=hashnode&utm_medium=feed) [danieljasme](https://hashnode.com/@danieljasme) · [Jul 3](https://hashnode.com/posts/building-an-ai-powered-tiktok-wellness-analytics-dashboard-my-final-year-project/6a478844650cdbee729d6311) ### [Building an AI-Powered TikTok Wellness Analytics Dashboard: My Final Year Project](https://danieljasme.hashnode.dev/building-an-ai-powered-tiktok-wellness-analytics-dashboard-my-final-year-project?utm_source=hashnode&utm_medium=feed) How I analysed 11,244 TikTok Shop wellness products with XGBoost to build a persona-aware dashboard for affiliates and small business owners. 10 min read · [danieljasme.hashnode.dev](https://danieljasme.hashnode.dev/building-an-ai-powered-tiktok-wellness-analytics-dashboard-my-final-year-project?utm_source=hashnode&utm_medium=feed) [Rishikant](https://hashnode.com/@rishiii2) · [Jun 28](https://hashnode.com/posts/conquering-tabular-data-decision-trees-random-forests-and-the-math-of-xgboost/6a4195b0b1400629a7b7c5b1) ### [Conquering Tabular Data: Decision Trees, Random Forests, and the Math of XGBoost](https://rishiii2.hashnode.dev/conquering-tabular-data-decision-trees-random-forests-and-the-math-of-xgboost?utm_source=hashnode&utm_medium=feed) Why Neural Networks aren't always the answer. A deep dive into Entropy, Information Gain, Bootstrapping, and Gradient Boosting. 7 min read · [rishiii2.hashnode.dev](https://rishiii2.hashnode.dev/conquering-tabular-data-decision-trees-random-forests-and-the-math-of-xgboost?utm_source=hashnode&utm_medium=feed)