Pages that link to "Item:Q127532"

The following pages link to Greedy function approximation: A gradient boosting machine. (Q127532):

Displaying 50 items.

• Optimal Individualized Decision Rules Using Instrumental Variable Methods (Q5857139) (← links)
• A Markov-modulated tree-based gradient boosting model for auto-insurance risk premium pricing (Q5858899) (← links)
• A multivariate multiple third-variable effect analysis with an application to explore racial and ethnic disparities in obesity (Q5861544) (← links)
• Optimization by Gradient Boosting (Q5870986) (← links)
• Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link (Q5872567) (← links)
• LocalGLMnet: interpretable deep learning for tabular data (Q5878643) (← links)
• AdaBoost Semiparametric Model Averaging Prediction for Multiple Categories (Q5881103) (← links)
• What are the Most Important Statistical Ideas of the Past 50 Years? (Q5881991) (← links)
• A Statistical Approach to Crime Linkage (Q5884429) (← links)
• Deep learning: a statistical viewpoint (Q5887827) (← links)
• Stochastic gradient boosting. (Q5958470) (← links)
• Looking for lumps: boosting and bagging for density estimation. (Q5958471) (← links)
• Improving nonparametric regression methods by bagging and boosting. (Q5958473) (← links)
• Signal approximations based on nonlinear and optimal piecewise affine functions (Q6044429) (← links)
• A machine learning approach to construct quarterly data on intangible investment for Eurozone (Q6047408) (← links)
• Nonparametric variable importance assessment using machine learning techniques (Q6047783) (← links)
• A hybrid ensemble method with negative correlation learning for regression (Q6053807) (← links)
• Adaptive Bayesian Sum of Trees Model for Covariate-Dependent Spectral Analysis (Q6055754) (← links)
• Local interpretation of supervised learning models based on high dimensional model representation (Q6057385) (← links)
• Hierarchical fuzzy regression tree: a new gradient boosting approach to design a TSK fuzzy model (Q6059602) (← links)
• Estimating propensity scores using neural networks and traditional methods: a comparative simulation study (Q6061348) (← links)
• Lasso regularization within the LocalGLMnet architecture (Q6062813) (← links)
• Margin optimal classification trees (Q6065640) (← links)
• Supervised Machine Learning Techniques: An Overview with Applications to Banking (Q6067148) (← links)
• Interpretable machine learning for imbalanced credit scoring datasets (Q6069240) (← links)
• GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes (Q6071715) (← links)
• Fully corrective gradient boosting with squared hinge: fast learning rates and early stopping (Q6072435) (← links)
• Boosting Distributional Copula Regression (Q6079714) (← links)
• Stochastic local search and parameters recommendation: a case study on flowshop problems (Q6079864) (← links)
• Explainable subgradient tree boosting for prescriptive analytics in operations management (Q6087515) (← links)
• A sequential modeling approach for predicting clinical outcomes with repeated measures (Q6096206) (← links)
• Gradient boosting with extreme-value theory for wildfire prediction (Q6100555) (← links)
• Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method (Q6103193) (← links)
• Stochastic Tree Ensembles for Regularized Nonlinear Regression (Q6107230) (← links)
• Tests and classification methods in adaptive designs with applications (Q6107661) (← links)
• Adaptive solution prediction for combinatorial optimization (Q6112875) (← links)
• Day-ahead aircraft routing with data-driven primary delay predictions (Q6113341) (← links)
• \(R^{\ast}\): a robust MCMC convergence diagnostic with uncertainty using decision tree classifiers (Q6121679) (← links)
• Infinitesimal gradient boosting (Q6123287) (← links)
• SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting (Q6134328) (← links)
• Mixture of inhomogeneous matrix models for species‐rich ecosystems (Q6139123) (← links)
• Gradient boosting for extreme quantile regression (Q6144813) (← links)
• Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making (Q6156851) (← links)
• On the robustness of sparse counterfactual explanations to adverse perturbations (Q6156854) (← links)
• Downscaling shallow water simulations using artificial neural networks and boosted trees (Q6160658) (← links)
• Gradient boosting for convex cone predict and optimize problems (Q6161287) (← links)
• A comparison of two dissimilarity functions for mixed-type predictor variables in the \(\delta\)-machine (Q6161660) (← links)
• Estimating global and country-specific excess mortality during the COVID-19 pandemic (Q6161878) (← links)
• Estimation and inference of treatment effects with \(L_2\)-boosting in high-dimensional settings (Q6163259) (← links)
• 2-step gradient boosting approach to selectivity bias correction in tax audit: an application to the VAT gap in Italy (Q6163506) (← links)