Understanding Gradient Boosting Classifier: Training, Prediction, and the Role of $γ_j$

• URL: http://arxiv.org/abs/2410.05623v2
• Date: Wed, 23 Oct 2024 07:28:19 GMT
• Title: Understanding Gradient Boosting Classifier: Training, Prediction, and the Role of $γ_j$
• Authors: Hung-Hsuan Chen,
• Abstract summary: The Gradient Boosting (GBC) is a widely used machine learning algorithm for binary classification. This document explains the training and prediction processes, focusing on the computation of terminal node values. We provide a step-by-step example in the appendix to help readers understand.
• Score: 2.44755919161855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Gradient Boosting Classifier (GBC) is a widely used machine learning algorithm for binary classification, which builds decision trees iteratively to minimize prediction errors. This document explains the GBC's training and prediction processes, focusing on the computation of terminal node values $\gamma_j$, which are crucial to optimizing the logistic loss function. We derive $\gamma_j$ through a Taylor series approximation and provide a step-by-step pseudocode for the algorithm's implementation. The guide explains the theory of GBC and its practical application, demonstrating its effectiveness in binary classification tasks. We provide a step-by-step example in the appendix to help readers understand.

Related papers

• Solving a Class of Cut-Generating Linear Programs via Machine Learning [0.0]
  Cut-generating linear programs (CGLPs) play a key role as a separation oracle to produce valid inequalities for the feasible region of mixed-integer programs. Running the dualPs at the nodes of the branch-and-bound tree is computationally cumbersome due to the number of node candidates and the lack of a priori knowledge on which nodes admit useful cutting planes. We propose a novel framework based on learning to approximate the optimal value of a machineP class that determines whether a cutting plane can be generated at a node of the branch-bound tree.
  arXiv  Detail & Related papers  (2023-10-30T18:31:52Z)
• A Log-linear Gradient Descent Algorithm for Unbalanced Binary Classification using the All Pairs Squared Hinge Loss [0.0]
  We propose a new functional representation of the square loss and squared hinge loss, which results in algorithms that compute the gradient in either linear or log-linear time. Our new algorithm can achieve higher test AUC values on imbalanced data sets than previous algorithms, and make use of larger batch sizes than were previously feasible.
  arXiv  Detail & Related papers  (2023-02-21T23:35:00Z)
• Robust Methods for High-Dimensional Linear Learning [0.0]
  We propose statistically robust and computationally efficient linear learning methods in the high-dimensional batch setting. We instantiate our framework on several applications including vanilla sparse, group-sparse and low-rank matrix recovery. For vanilla $s$-sparsity, we are able to reach the $slog (d)/n$ rate under heavy-tails and $eta$-corruption.
  arXiv  Detail & Related papers  (2022-08-10T17:00:41Z)
• Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling? [59.820507600960745]
  We propose a new GCP meta-layer that uses SVD in the forward pass, and Pad'e Approximants in the backward propagation to compute the gradients. The proposed meta-layer has been integrated into different CNN models and achieves state-of-the-art performances on both large-scale and fine-grained datasets.
  arXiv  Detail & Related papers  (2021-05-06T08:03:45Z)
• Correcting Momentum with Second-order Information [50.992629498861724]
  We develop a new algorithm for non-critical optimization that finds an $O(epsilon)$epsilon point in the optimal product. We validate our results on a variety of large-scale deep learning benchmarks and architectures.
  arXiv  Detail & Related papers  (2021-03-04T19:01:20Z)
• Learning Sign-Constrained Support Vector Machines [0.24466725954625884]
  We develop two optimization algorithms for minimizing the empirical risk under the sign constraints. One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes $O(nd)$ computational cost. We empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.
  arXiv  Detail & Related papers  (2021-01-05T12:08:17Z)
• Fast Few-Shot Classification by Few-Iteration Meta-Learning [173.32497326674775]
  We introduce a fast optimization-based meta-learning method for few-shot classification. Our strategy enables important aspects of the base learner objective to be learned during meta-training. We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-10-01T15:59:31Z)
• Information Theoretic Meta Learning with Gaussian Processes [74.54485310507336]
  We formulate meta learning using information theoretic concepts; namely, mutual information and the information bottleneck. By making use of variational approximations to the mutual information, we derive a general and tractable framework for meta learning.
  arXiv  Detail & Related papers  (2020-09-07T16:47:30Z)
• Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension [124.7752517531109]
  We establish a provably efficient reinforcement learning algorithm with general value function approximation. We show that our algorithm achieves a regret bound of $widetildeO(mathrmpoly(dH)sqrtT)$ where $d$ is a complexity measure. Our theory generalizes recent progress on RL with linear value function approximation and does not make explicit assumptions on the model of the environment.
  arXiv  Detail & Related papers  (2020-05-21T17:36:09Z)
• Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping [29.485528641599018]
  We propose an efficient boosting method with theoretical generalization guarantees for binary classification. We derive a fast learning rate of the order $cal O((m/log m)-1/4)$ for the proposed boosting method. Both derived learning rates are the best ones among the existing generalization results of boosting-type methods for classification.
  arXiv  Detail & Related papers  (2020-04-01T00:39:24Z)
• Variance Reduction with Sparse Gradients [82.41780420431205]
  Variance reduction methods such as SVRG and SpiderBoost use a mixture of large and small batch gradients. We introduce a new sparsity operator: The random-top-k operator. Our algorithm consistently outperforms SpiderBoost on various tasks including image classification, natural language processing, and sparse matrix factorization.
  arXiv  Detail & Related papers  (2020-01-27T08:23:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.