Feb 5
الذكاء الاصطناعي يتنبأ بالإجهاد الحراري للشعاب المرجانية قبل حدوث التبييض
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الذكاء الاصطناعي يتنبأ بالإجهاد الحراري للشعاب المرجانية قبل حدوث التبييض
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Cattle farming are essentially based on the exploitation of natural pastures. Determining the distances traveled by animals in extensive livestock farming remains a major challenge in the West African sub-region. The objective of this study was to develop a robust predictive model of the daily distance traveled by animals in pastoral systems, exploiting the capabilities of the XGBoost algorithm. This study evaluated pastoral cattle movements in two municipalities in different agroecological zones of Burkina Faso. Tracking carried out with mini-GPS devices made it possible to collect positioning data on the movement of grazing animals over three distinct seasons. The daily distance traveled was modeled with an XGBoost algorithm whose performance was validated by cross-validation. Post-modeling analyses then made it possible to interpret the model and identify factors influencing herd behavior. Performance was good, with a coefficient of determination R of 0.92 and an average error of only 245 meters. Variable importance analysis revealed a dominance of grazing time (69.4%) and travel speed (22.3%) in predicting distances traveled. The XGBoost method offers a robust alternative to traditional statistical models. This approach opens up concrete prospects for the development of decision-support tools that enable livestock farmers and managers to more effectively plan rangeland use, reduce environmental impact, and improve animal welfare.
🏷 AI Models Explained: Gradient Boosting (XGBoost, LightGBM, CatBoost)
📖 Gradient Boosting is a next-level ensemble learning method that improves prediction accuracy by training models sequentially — each new tree fixes the errors of the previous one. Popular versions like XGBoost, LightGBM, and CatBoost are widely used in AI competitions and industry projects for their speed and precision.
1️⃣ The Foundations
Works by boosting weak learners (usually Decision Trees) to form a strong predictive model.
Each new model is trained to correct residual errors of the previous one.
Variants:
XGBoost – Extremely efficient and regularized boosting.
LightGBM – Fast and optimized for large datasets.
CatBoost – Handles categorical data automatically.
2️⃣ Where It’s Used
Finance: Fraud detection, credit scoring.
Marketing: Customer segmentation and response prediction.
E-commerce: Product ranking and recommendation engines.
Healthcare: Disease prediction and risk analysis.
3️⃣ Strengths vs Limitations
Strengths
Excellent predictive accuracy.
Handles complex, non-linear relationships.
Supports large datasets efficiently.
Limitations
Computationally heavy for very large models.
Harder to interpret than simpler algorithms.
Requires careful hyperparameter tuning.
4️⃣ Pro Tips
Use early stopping to prevent overfitting.
Tune learning rate (η), tree depth, and number of estimators.
Try LightGBM for speed and CatBoost for categorical data.
Monitor metrics like AUC and RMSE for performance tracking.
💡 Final Note Gradient Boosting combines precision and power, making it one of the most competitive algorithms in machine learning. It’s the driving force behind many Kaggle-winning models and modern AI applications.
📌 Series Continuation This is Day 6 of the AI Models Explained series 🎉. Next up: K-Nearest Neighbors (KNN) – Learning by Proximity and Patterns.
Stay tuned with Uplatz as we continue exploring AI models, one at a time 🚀
XGBoost Feature Importance Explained - Top Tips
Unlocking Insights: Interpreting Your XGBoost Model In the world of machine learning, building accurate models is just the first step. Truly understanding why your model makes its predictions – and identifying which factors are driving those decisions – is crucial for effective deployment and refinement. XGBoost (Extreme Gradient Boosting), a popular and powerful algorithm, provides several…
Project Title:Cross-Platform E-commerce Revenue Attribution Modeling using Pandas, XGBoost, and Attribution Modeling Algorithms
Project Title:Cross-Platform E-commerce Revenue Attribution Modeling using Pandas, XGBoost, and Attribution Modeling AlgorithmsReference ID: ai-ml-ds-SrmZNuoOhMkFile Name: cross_platform_ecommerce_revenue_attribution_modeling.py Short Description: This project aims to develop an advanced, cross-platform e-commerce revenue attribution model. By leveraging Pandas for data manipulation, XGBoost…
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Project Title:Cross-Platform E-commerce Revenue Attribution Modeling using Pandas, XGBoost, and Attribution Modeling Algorithms
Project Title:Cross-Platform E-commerce Revenue Attribution Modeling using Pandas, XGBoost, and Attribution Modeling AlgorithmsReference ID: ai-ml-ds-SrmZNuoOhMkFile Name: cross_platform_ecommerce_revenue_attribution_modeling.py Short Description: This project aims to develop an advanced, cross-platform e-commerce revenue attribution model. By leveraging Pandas for data manipulation, XGBoost…
Project Title:Cross-Platform E-commerce Revenue Attribution Modeling using Pandas, XGBoost, and Attribution Modeling Algorithms
Project Title:Cross-Platform E-commerce Revenue Attribution Modeling using Pandas, XGBoost, and Attribution Modeling AlgorithmsReference ID: ai-ml-ds-SrmZNuoOhMkFile Name: cross_platform_ecommerce_revenue_attribution_modeling.py Short Description: This project aims to develop an advanced, cross-platform e-commerce revenue attribution model. By leveraging Pandas for data manipulation, XGBoost…
Project Title:Cross-Platform E-commerce Revenue Attribution Modeling using Pandas, XGBoost, and Attribution Modeling Algorithms
Project Title:Cross-Platform E-commerce Revenue Attribution Modeling using Pandas, XGBoost, and Attribution Modeling AlgorithmsReference ID: ai-ml-ds-SrmZNuoOhMkFile Name: cross_platform_ecommerce_revenue_attribution_modeling.py Short Description: This project aims to develop an advanced, cross-platform e-commerce revenue attribution model. By leveraging Pandas for data manipulation, XGBoost…