Results 51 to 60 of about 88,463 (183)

Fast calibrated additive quantile regression

, 2020
We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation of the smoothing parameters, for model structures as diverse as those ...
Azzalini A., Brent R. P., Hastie T., Hothorn T., Jones M., Margaux Zaffran, Matteo Fasiolo, Raphaël Nedellec, Robert C., Simon N. Wood, Yannig Goude, Yee T. W.   +11 more
core   +1 more source

A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context

Risks
The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine ...
Praiya Panjee, Sataporn Amornsawadwatana
doaj   +1 more source

The Error is the Feature: how to Forecast Lightning using a Model Prediction Error

, 2019
Despite the progress within the last decades, weather forecasting is still a challenging and computationally expensive task. Current satellite-based approaches to predict thunderstorms are usually based on the analysis of the observed brightness ...
Andersson T., Bott Andreas, Ernst Ludwig Planck Max Karl, Lang P, Ruiz Anne, Veillette Mark S., Williams John K., Zach Christopher   +7 more
core   +1 more source

Formal Verification of Input-Output Mappings of Tree Ensembles

, 2020
Recent advances in machine learning and artificial intelligence are now being considered in safety-critical autonomous systems where software defects may cause severe harm to humans and the environment. Design organizations in these domains are currently
Nadjm-Tehrani, Simin, Törnblom, John
core   +1 more source

Machine learning regression models for internal shame

Acta Psychologica
This study aims to predict Internal Shame (IS) based on childhood trauma, social emotional competence, cognitive flexibility, distress tolerance and alexithymia in an Iranian sample.
Nataša Kovač, Kruna Ratković, Hojjatollah Farahani, Peter Watson   +3 more
doaj   +1 more source

A novel improved model for building energy consumption prediction based on model integration [PDF]

, 2020
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems.
Feng, W, Lu, S, Wang, R
core  

Predicting time to graduation at a large enrollment American university

, 2020
The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend ...
Aiken, John M., Caballero, Marcos D., De Bin, Riccardo, Hjorth-Jensen, Morten   +3 more
core   +1 more source

A Proposed Framework for Early Prediction of Schistosomiasis

Diagnostics, 2022
Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body.
Zain Ali, Muhammad Faisal Hayat, Kamran Shaukat, Talha Mahboob Alam, Ibrahim A. Hameed, Suhuai Luo, Shakila Basheer, Manel Ayadi, Amel Ksibi   +8 more
doaj   +1 more source

Analisis Model Prediksi Penyakit Jantung Menggunakan Adaptive Boosting, Gradient Boosting, dan Extreme Gradient Boosting

Jurnal Ilmiah FIFO
Deteksi dini penyakit jantung merupakan langkah penting untuk meningkatkan kualitas diagnosis dan perawatan pasien. Namun, metode prediksi manual yang sering digunakan tenaga medis memiliki keterbatasan dalam efisiensi waktu, akurasi, dan kemampuan menangani volume data yang besar.
Andrian Sah, Chaeroen Niesa, Rhaishudin Rumandan Jafar, Muhammad Muharrom   +3 more
openaire   +1 more source

Learning Nonlinear Functions Using Regularized Greedy Forest

, 2013
We consider the problem of learning a forest of nonlinear decision rules with general loss functions. The standard methods employ boosted decision trees such as Adaboost for exponential loss and Friedman's gradient boosting for general loss.
Johnson, Rie, Zhang, Tong
core   +2 more sources