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Category:All HeadlinesWorld & GeopoliticsMarkets & EconomyTech & AIPoliticsSearch results for: "Mixed-Effects XGBoost wit" (22 stories)

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Mixed-Effects XGBoost with Group-Aware Permutation Importance and Cross-Validation for Multilevel Cross-Classified Continuous Outcomes | Psychometrika
Lead StoryCambridge University Press & Assessmentgeneral
Apr 10

Mixed-Effects XGBoost with Group-Aware Permutation Importance and Cross-Validation for Multilevel Cross-Classified Continuous Outcomes | Psychometrika

<a href="https://news.google.com/rss/articles/CBMiyAJBVV95cUxOY2FOSTVTb0xMZkpFendKSFpiLUt4WU02NGZHZG1tcnF4UGNCd3psZ0VIOFZWc0M1TWtlZnk0SDJpTkJWcTBtb0R5bDFfcHpNWXVENjZjcFhIQUNONUVSYm9TeUliX2h4aEV3dlg2elgyMDlSMkhWLTM5TEZVRDBzdG1QWnpKWWlNRGI5UGJDbmFRQnRUU2piQV9xdmFhd3QxdXRIM25UNlQ0MExzNlZFdVZ3bDVoMWVSX1RLRlVma2tWTEs4aHdDZlUzSkZENU9rSlZKeGhNQXd5TE02VF8yV09EUVhGY3gyZkRFaldMbTJGTzhoWWlVbVVUa3NmRklTeGJJaEFsczA4N2YxRmpvZ1luV1ZxZkxyVUZGQjNEVXgwaXYydWtDOElpTm9hbzdyT1ZSM3k2LXhxOFdsZXlQTHVIbWxBRTZR?oc=5" target="_blank">Mixed-Effects XGBoost with Group-Aware Permutation Importance and Cross-Validation for Multilevel Cross-Classified Continuous Outcomes | Psychometrika</a>  <font color="#6f6f6f">Cambridge University Press & Assessment</font>

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Search Results for "Mixed-Effects XGBoost wit"

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21 stories displayed
Integrated CNN–LSTM–XGBoost hybrid model predicts shale oil seismic attributes and global oil price trends
Naturegeneral

Integrated CNN–LSTM–XGBoost hybrid model predicts shale oil seismic attributes and global oil price trends

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1WNHBWRWcwRndfbzdITnlmMmZjWXFON0pIM1F1dmREMXJjNjNIOVZpMUVId2N5dXp4MVZWeGgtXzNpLV9jN1o2LW01Y0JFNXhyczdGZzRTakxtSGVmN0lv?oc=5" target="_blank">Integrated CNN–LSTM–XGBoost hybrid model predicts shale oil seismic attributes and global oil price trends</a>  <font color="#6f6f6f">Nature</font>

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Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation
Frontierstech

Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation

<a href="https://news.google.com/rss/articles/CBMilAFBVV95cUxOdEZrU0RVdDRmbnduY0dXZjBxLUlzQk5aMTJxa29RZHhmTEZXVnhPQ04tS1RBMWdfVklHNHJmRldRUmg5RXU0QVJVNnVvVTgtTEx4dkw1NFl1eVBFZC1kTWhaZVRVV2x1d0ZpWU94Tl9HZjh0RG5sdkNFcFUxelVOMWhfSkhMa3A5YlkybDc4S1poSHp0?oc=5" target="_blank">Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation</a>  <font color="#6f6f6f">Frontiers</font>

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Tree-Boosted Mixed Effects Models
Towards Data Sciencegeneral

Tree-Boosted Mixed Effects Models

<a href="https://news.google.com/rss/articles/CBMihAFBVV95cUxNbjMtX1FLdTk3YVZFM2NCd1VLTTVaS2FEQXltYkVxcy1NQXBqWWI3aWZqSEFxZElNNzJIa1JKOXR0cXM1XzlLSkhiR2tPRGVzTU9fb1FGVGJmM3pISW1QaC1tbmg5LTVseDZOR0JZc21CNlJRMWJZUEdaU3ZfcWFZVXdJTGE?oc=5" target="_blank">Tree-Boosted Mixed Effects Models</a>  <font color="#6f6f6f">Towards Data Science</font>

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Ensemble machine learning framework with SHAP and LIME for accurate early prediction of student success in online learning environments
Naturegeneral

Ensemble machine learning framework with SHAP and LIME for accurate early prediction of student success in online learning environments

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9UTTh4aGxMSVBfYTZxR1ZON2FDZ2RoZVBLQ3JzLXNXZWJ6NjJUUENJMzZrelI5UVc3ZG5fQS1jZnk4SHg1RHdjOGt4dHBpSVNVQnBJVTdYNnB1LWw5MHF3?oc=5" target="_blank">Ensemble machine learning framework with SHAP and LIME for accurate early prediction of student success in online learning environments</a>  <font color="#6f6f6f">Nature</font>

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A stacked Gradient Boosting–XGBoost ensemble with ridge meta-learner for accurate short-term solar PV power forecasting in smart grids
Naturegeneral

A stacked Gradient Boosting–XGBoost ensemble with ridge meta-learner for accurate short-term solar PV power forecasting in smart grids

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9sNWh1bXJ3eVgteDFXck9NcGdZRmpjMG5xUWRqbjYtLXAySldhM3ctYmJPWTlyaWVCWGozbV9CaTJEbWFxcE9KTkZscEl1WW1VMmFlRHNNT2RHNC1sNWdR?oc=5" target="_blank">A stacked Gradient Boosting–XGBoost ensemble with ridge meta-learner for accurate short-term solar PV power forecasting in smart grids</a>  <font color="#6f6f6f">Nature</font>

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Electric vehicles charging stations load forecasting based on hybrid XGBoost-BiLSTM model
Naturegeneral

Electric vehicles charging stations load forecasting based on hybrid XGBoost-BiLSTM model

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBUT1MwR241Vy1rVFc0WVBWX0NieUwxTkJyaGpxbHVScjdLcl9PSnBJb2I0SEZ2Tl9IUU5TajJtdGhqbnBhbWtVYmNkSDR2cFc4NDFiZjZCSlhXdmh4bnZR?oc=5" target="_blank">Electric vehicles charging stations load forecasting based on hybrid XGBoost-BiLSTM model</a>  <font color="#6f6f6f">Nature</font>

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Optimised hybrid late fusion deep learning model for cashew disease classification
Frontiersgeneral

Optimised hybrid late fusion deep learning model for cashew disease classification

<a href="https://news.google.com/rss/articles/CBMilAFBVV95cUxQS0t4d2E1ZTdWWHN0LUMzdU9NTDdrZ3lDUGN1WHI2RThwVU1aclcwTHV0SDlJemJzMDhTWEZsZXZTSnhUUkozMFdySU56M3BsRmJGWkVPdGNxeUZMZHl5MkNpTWdQaUY5VUtCaFEzSnB3VUx6b094REVaRDJud1VlcEJ5MS0zWG5VTXdHUFhrbGhVY2tW?oc=5" target="_blank">Optimised hybrid late fusion deep learning model for cashew disease classification</a>  <font color="#6f6f6f">Frontiers</font>

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Mechanical properties and durability of concrete with zeolite and waste ceramic powder through experimental investigation and machine learning analysis
Naturegeneral

Mechanical properties and durability of concrete with zeolite and waste ceramic powder through experimental investigation and machine learning analysis

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBsVGVqWm5ENkpHVnVaTV8wRWlRRU9TdUVJUXl0NjNzVmRrUDBfWG1IaFZXM1p0WWktNTVWNHFFUXotOC00M0FqVXlWNk1ZR1llenZ6QXlmTDdBWDJPcV84?oc=5" target="_blank">Mechanical properties and durability of concrete with zeolite and waste ceramic powder through experimental investigation and machine learning analysis</a>  <font color="#6f6f6f">Nature</font>

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Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability
Naturegeneral

Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1COEFZM3M5cTFtWFFqNkFhWkdSVFllMVZSZUhfekFraG5lMTVjckZSbXl0N1FKQ3ZZNm43ZWtQa3FTdTVHYWpPN2pTMndqYmJ1dm5vYVdYbndfUFg5dXJF?oc=5" target="_blank">Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability</a>  <font color="#6f6f6f">Nature</font>

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RETRACTED ARTICLE: Precision biochar yield forecasting employing random forest and XGBoost with Taylor diagram visualization
Naturegeneral

RETRACTED ARTICLE: Precision biochar yield forecasting employing random forest and XGBoost with Taylor diagram visualization

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5Yd29SR21PUVJnQXZ2eUJmVzdoeThmQ0lDbmprUXRGWlJEeFk5SHNsSWRHMDVYalFJWHN5cm8yQVlpZ3FpRkIwMDU0ajdUVVpEVVd3MGZERDZrUTBEbTcw?oc=5" target="_blank">RETRACTED ARTICLE: Precision biochar yield forecasting employing random forest and XGBoost with Taylor diagram visualization</a>  <font color="#6f6f6f">Nature</font>

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A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications
Naturegeneral

A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9PVzB3ZzFOMDZla3JBVFlGUnVoUlV5b0dBaW9HUU1paklPMkFpeXlDOFE1NE9iU3dzVXZ6MGQ3ajNPakQyZHVOb29MU0RYeTFLdlQyOTRZamVaQ2M0UklB?oc=5" target="_blank">A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications</a>  <font color="#6f6f6f">Nature</font>

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Parallel fusion model for complex multi-source vibration time-series prediction
Naturegeneral

Parallel fusion model for complex multi-source vibration time-series prediction

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5DMW5GSHFuckJpYWk0NnJMTVk4OHliWjVGVVJNVnRmSkE5RDU0SDRjcXMxZlRkcGVsTWdGWFFHSldDcjRiek8ybGIzZHIwMzNldVpMcWxOenlDQXdRUTBz?oc=5" target="_blank">Parallel fusion model for complex multi-source vibration time-series prediction</a>  <font color="#6f6f6f">Nature</font>

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Evaluating human–machine collaboration through a comparative analysis of experts, machine learning, and hybrid approaches in real estate valuation | Scientific Reports
Naturegeneral

Evaluating human–machine collaboration through a comparative analysis of experts, machine learning, and hybrid approaches in real estate valuation | Scientific Reports

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBsMnMydUtQUFN2OHJKTGlHZkxWYjBqdWFNR1BzdFVIRFZsZVFBR1NSdU5KMGR1V19WYlpWenh5M19hV0hEeHBVcjlLQ1dXRHJvR01ydDVJT285WERMdURr?oc=5" target="_blank">Evaluating human–machine collaboration through a comparative analysis of experts, machine learning, and hybrid approaches in real estate valuation | Scientific Reports</a>  <font color="#6f6f6f">Nature</font>

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Mitigating class imbalance in churn prediction with ensemble methods and SMOTE
Naturegeneral

Mitigating class imbalance in churn prediction with ensemble methods and SMOTE

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1JNW1JRkRRZ3U1MjdHOGpPTnhIbnZNMERkVm9SOHVORDJHWkhkQWtJV2Y5MFhxVXJzOWhMcWUtOXFwR18xdzAtZElfM3Q2ZjBjU0wxc2p1ekxEMF8xd2tN?oc=5" target="_blank">Mitigating class imbalance in churn prediction with ensemble methods and SMOTE</a>  <font color="#6f6f6f">Nature</font>

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A Hybrid Approach to Attention Deficit Hyperactivity Disorder Detection Leveraging Transformer and XGBoost Models Using XSparseFormerNet
Naturegeneral

A Hybrid Approach to Attention Deficit Hyperactivity Disorder Detection Leveraging Transformer and XGBoost Models Using XSparseFormerNet

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBvc3BYMVctV2RmdFEzT1hOMkhzSFNhZkVycy1fdXRSWnZVcnBrUS1LY0FIVlYtalFNY1RvMV9pbW5kTVlPUkI4X2U4UTVCRVBTZmtLMkpUTEIyUWFibE1J?oc=5" target="_blank">A Hybrid Approach to Attention Deficit Hyperactivity Disorder Detection Leveraging Transformer and XGBoost Models Using XSparseFormerNet</a>  <font color="#6f6f6f">Nature</font>

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ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds
Naturegeneral

ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1JZmZkaVYzLU1fR2RLMFdGUjNTUjFENlpaV0pBUk5LUGZGNHV6X2l2OVdjbFFEOHFmS1pvRnVCSDljT1c1X1JvdTZ3cG5PbmtvVVp0UkNKeVJMV0lFdGgw?oc=5" target="_blank">ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds</a>  <font color="#6f6f6f">Nature</font>

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Progression subtypes in Parkinson’s disease identified by a data-driven multi cohort analysis
Naturegeneral

Progression subtypes in Parkinson’s disease identified by a data-driven multi cohort analysis

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5PczB1eUZ3ZXVya3IwUWtjYmUyYmVqLXZUakUzQVhmaDNuYXJJWHZwRjRPTEJwbktQU3ktRi1xNXFKV1RPOGFwOWlPS3NtUXJpV1lES3hjX1ROdnBXYnBB?oc=5" target="_blank">Progression subtypes in Parkinson’s disease identified by a data-driven multi cohort analysis</a>  <font color="#6f6f6f">Nature</font>

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A Novel Hybrid Approach To Drought Forecasting: Leveraging Feature Engineering And Ensemble Methods
Naturegeneral

A Novel Hybrid Approach To Drought Forecasting: Leveraging Feature Engineering And Ensemble Methods

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE96LXpZbEdJaG55c1I5d01LdXNMYldnYWRGUjRESmFCaTNuRlI5d2pXR1NJamtjcXdkOWNXX21EVldmUTJPSS0ydGR3WU5vWnlDN01jU3BoTkdmY1RTTmdV?oc=5" target="_blank">A Novel Hybrid Approach To Drought Forecasting: Leveraging Feature Engineering And Ensemble Methods</a>  <font color="#6f6f6f">Nature</font>

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Mix design and performance prediction of EPS lightweight structural concrete based on orthogonal experimentation
Naturegeneral

Mix design and performance prediction of EPS lightweight structural concrete based on orthogonal experimentation

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE0xNjBnV3dLM0VvbGVWM1dXRU9rT0EzWVFaX242c0F3Y29rRk5EUS1ENTBZb3FnWGFPN012OS1vSWhqV1ZIVnpKRlpybk9idWFaUGY5RHRnVHBlTWJVRFpn?oc=5" target="_blank">Mix design and performance prediction of EPS lightweight structural concrete based on orthogonal experimentation</a>  <font color="#6f6f6f">Nature</font>

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How can XGBoost support natively categories?
Towards Data Sciencegeneral

How can XGBoost support natively categories?

<a href="https://news.google.com/rss/articles/CBMiowFBVV95cUxPRVFSVGVTSzZkVEdvR1JpZncxdnEyak5rQ2NTQVN2NTFqTXhQalZGOXVKMDZLbmp6ZGdrc3BfRUlPcXFFQVZYeEtPUVpfdjI1YnBYLTY2c1JvSVFuM2ppNGVxQ2RnUlZYenZ2NVh5ODFCZTA2SDdpSzRQX1RjMVp2eWhkUE9DUHF2cUlwaFFwbW1QMGtjUzZUZXRGUS1xcjlyYTB3?oc=5" target="_blank">How can XGBoost support natively categories?</a>  <font color="#6f6f6f">Towards Data Science</font>

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Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations | Communications Biology
Natureworld

Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations | Communications Biology

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5lYlZJb1FzSXA4VXRjdkhGZzhKZExic002NWNNVU93TUkxdzEyWTFVZVJaVUtLVTkyNTFROThXSDNPSE1XMUtDeC04X3pzaGJMQ2JFd1A5OEE1WEhwQlFz?oc=5" target="_blank">Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations | Communications Biology</a>  <font color="#6f6f6f">Nature</font>

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