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regression

Papers

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Showing 9176–9200 of 9424 papers

TitleStatusHype
Elastic-net Regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signals Detection—0
Electoral David vs Goliath: How does the Spatial Concentration of Electors affect District-based Elections?—0
Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine—0
Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression—0
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression—0
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis—0
Electrophysiological indicators of gesture perception—0
Elite Bases Regression: A Real-time Algorithm for Symbolic Regression—0
_1-regression with Heavy-tailed Distributions—0
_1-regression with Heavy-tailed Distributions—0
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering—0
Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion—0
Ellipse Regression with Predicted Uncertainties for Accurate Multi-View 3D Object Estimation—0
_p-Regression in the Arbitrary Partition Model of Communication—0
ELM Ridge Regression Boosting—0
Embedded Multi-label Feature Selection via Orthogonal Regression—0
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators—0
Embeddings for Tabular Data: A Survey—0
Embryonic Elongation controlled by Graded Cell Fate: A Molecular Dynamics Approach—0
EM Converges for a Mixture of Many Linear Regressions—0
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems—0
EM for Mixture of Linear Regression with Clustered Data—0
EMLight: Lighting Estimation via Spherical Distribution Approximation—0
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets—0
EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM—0
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All datasetsCalifornia Housing PricesConcrete Compressive StrengthMedical Cost Personal DatasetSynthetic: y = x * sin xADORECar_Price_Prediction

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.79—Unverified
2Baseline RegressionR2 Score0.58—Unverified
3Mimic / SurrogateR2 Score0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.86—Unverified
2Baseline RegressionR2 Score0.59—Unverified
3Mimic / SurrogateR2 Score0.58—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.87—Unverified
2Baseline RegressionR2 Score0.78—Unverified
3Mimic / SurrogateR2 Score0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1Neural NetworkR2 Score0.98—Unverified
2Baseline RegressionR2 Score-0.01—Unverified
3Mimic / SurrogateR2 Score-0.01—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoost-MACCSmicro-averaged RMSE0.93—Unverified
2RF-ToxPrintchemical macro-average RMSE0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1Linear and Decision Tree RegressionR Squared86.84—Unverified