A Comparative Analysis of Hyperopt as Against Other Approaches for Hyper-Parameter Optimization of XGBoost

Putatunda, Susil K.; Rama, Kiran

doi:10.1145/3297067.3297080

Cited by 102 publications

(57 citation statements)

References 16 publications

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“…19 (3) loss ( ) In this work, the CoxPH and the RSF were performed using the R packages, survival, and ran-domForestSRC, respectively, while DeepSurv by an open-source Python package. Hyperopt, a TensorFlow Python package, 20 was employed for Bayesian hyperparameter optimization.…”

Section: Deepsurvmentioning

confidence: 99%

The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

et al. 2021

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Section: Deepsurvmentioning

confidence: 99%

The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

et al. 2021

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“…These hyperparameters can be tuned with the help of random or exhaustive search as well as by using Bayesian optimization. The Bayesian optimization method has shown efficiency in terms of accuracy and time [ 51 ].…”

Section: Methodsmentioning

confidence: 99%

Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach

Mehta¹,

Julaiti²,

Griffin³

et al. 2020

JMIR Public Health Surveill

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Background The rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to quickly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread of this virus. Objective The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data. Methods We estimated county-level COVID-19 occurrences for the period March 14 to 31, 2020, based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features. We developed a three-stage model using XGBoost, a machine learning algorithm, to quantify the probability of COVID-19 occurrence and estimate the number of potential occurrences for unaffected counties. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county. Results The model predictions showed a sensitivity over 71% and specificity over 94% for models built using data from March 14 to 31, 2020. We found that population, population density, percentage of people aged >70 years, and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We observed a positive association at the county level between urbanicity and vulnerability to COVID-19. Conclusions The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduce significant variation in reported cases, which produces a bias in the model.

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“…In this section we try to tune the hyperparameters [17] for XGBoost classifiers for more accurate result. Here we get the best parameters for XGBoost classifier are -'gamma': 0, 'learning_rate': 0.1, 'max_depth': 12…”

Section: Figure 12: Roc Curve For Gradient Boostingmentioning

confidence: 99%

Applying Ensemble Approach on U.S. Census Data Classification

Pal¹,

Chaturvedi²,

Teja³

et al. 2021

IJCSMC

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A Comparative Analysis of Hyperopt as Against Other Approaches for Hyper-Parameter Optimization of XGBoost

Cited by 102 publications

References 16 publications

The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach

Applying Ensemble Approach on U.S. Census Data Classification

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A Comparative Analysis of Hyperopt as Against Other Approaches for Hyper-Parameter Optimization of XGBoost

Cited by 102 publications

References 16 publications

The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma

Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach

Applying Ensemble Approach on U.S. Census Data Classification﻿

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Product

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Applying Ensemble Approach on U.S. Census Data Classification