GitHub

Telco Customer Churn Prediction

Pipeline data analytics & machine learning end-to-end menggunakan AdaBoost, SMOTENC, Optuna, dan threshold tuning untuk memprediksi customer churn, mencapai recall 91,64% dan mengurangi biaya ...
KDnuggets

How to Access Multiple AI Models in Your Python Apps Using OpenRouter

Learn how to get started with the OpenRouter API to call multiple AI models through one API, compare responses, switch models ...
KDnuggets

7 Advanced Python Design Patterns for Data Scientists

Learn seven design patterns for data science code, from Strategy and Adapter to Command and Proxy, each matched to the ...
tko

Jak wybrać najlepszy Kurs Python AI Engineer?

Aby wybrać szkolenie, które stworzy realne perspektywy zawodowe i pozwoli opanować tworzenie systemów AI od podstaw do wdrożenia, należy zwrócić uwagę na zakres technologiczny, architekturę kodu oraz ...
blog.csdn

随机森林、Bagging 与 Boosting 集成学习深度解析：基于 Python Machine Learning 书籍仓库的偏差-方差权衡与实战验证

文章浏览阅读5次。本文基于《Python Machine Learning》开源仓库，系统解析Bagging、AdaBoost和随机森林三大集成学习方法，聚焦偏差-方差权衡原理：Bagging与随机森林通过并行训练降低方差（缓解过拟合），AdaBoost通过串行加权提升降低偏差（缓解欠拟合）；结合Wine数据集实战验证，展示scikit-learn中 ...
marktechpost

Google AI Introduces TabFM: A Hybrid-Attention Tabular Foundation Model for Zero-Shot Classification and Regression

Google Research introduced TabFM, a foundation model built for tabular data. TabFM performs classification and regression without dataset-specific training. Every prediction comes from a single ...
SciELO

Machine Learning Applied to Aquatic Toxicity and Chemical Reactions of Xenobiotic Compounds

The SMILES representation was modified and expanded to include new features, naming it SMiles ARbitrary Target Specification (SMARTS). 22 This coding is very specific and complex, with its own rules.
letsdatascience

AdaBoost: How Adaptive Boosting Reweights Mistakes

Vector search put a retired rule first. The current one came ninth. Build an on-prem AI agent that gets this right: filters inside the search, a local model, and memory that saves only what a person ...
letsdatascience

Gradient Boosting: Building Strong Models From Residuals

Gradient boosting builds accurate predictions by stacking small corrections on top of each other. The first model guesses the average house price. The second model looks at the leftover errors and ...
learn

Tutorial: model development on a cloud workstation

This article describes how to develop a training script by using a notebook on an Azure Machine Learning cloud workstation. The tutorial covers the basic steps that you need to get started: Set up and ...
builtin

Ensemble Models: What Are They and When Should You Use Them?

An ensemble model is a machine learning model that combines multiple individual models to improve prediction accuracy, as opposed to using just one model alone. Anytime we need to make an important ...
ascopubs.org

Preoperative Maximum Standardized Uptake Value Emphasized in Explainable Machine Learning Model for Predicting the Risk of Recurrence in Resected Non–Small Cell Lung Cancer

This retrospective study included 643 patients who had undergone NSCLC resection. ML models (random forest, gradient boosting, extreme gradient boosting, and AdaBoost) and a random survival forest ...