1.直方图算法(Histogram-based Algorithm) 2.单边梯度采样(Gradient-based One-Side Sampling,即 GOSS) 3.互斥特征绑定(Exclusive Feature Bundling,即 EFB) 4.Leaf-wise决策树生成策略 5.类别特征支持(Categorical Feature Support) 总结 参考 系列回顾
《LightGBM: A Highly Efficient Gradient Boosting Decision Tree》论文笔记 。3GOSS (Gradient-basedOne-SideSampling) 3.1算法描述 我们发现在GBDT中,每个数据实例的梯度对数据采样提供了有用的信息。 GOSS会保有带有大梯度的...,我们引入了两项技术:Gradient-basedOne-SideSampling(GOSS)andExclusiveFeatureBundling(E...
原因是决策树本来就是弱模型,分割点是不是精确并不是太重要;较粗的分割点也有正则化的效果,可以有效地防止过拟合;即使单棵树的训练误差比精确分割的算法稍大,但在梯度提升(Gradient Boosting)的框架下没有太大的影响。 Gradient-based One-Side Sampling 简而言之,GOSS保留了梯度较大的数据(这里有个理论,一般梯度...
3 GOSS(Gradient-based One-Side Sampling) 3.1 算法描述我们发现在GBDT中,每个数据实例的梯度对数据采样提供了有用的信息。 GOSS会保有带有大梯度的 Lightgbm源论文解析:LightGBM: A Highly Efficient Gradient Boosting Decision Tree )非常流行却鲜有实现,只有像XGBoost和pGBRT实现。当特征维度较高和数据量巨大的...
虽然说XGBoost相较于GBDT已经有了长足的进步,但是如果我们仔细去思考其整个算法流程,会发现其依然存在一些优化点,比如尽管在树分裂的过程中,XGBoost使用预先排序以及近似算法能够大幅提升效率,但是其依然需要遍历所有的样本,这样显然效率不够高,在LightGBM中,其提出了单边梯度采样(Gradient-based One-Side Sampling,GOSS)算...
Next one is Gradient-Based One-Side Sampling. This is a really cool one. Jon Krohn: 01:29:33 Oh, yeah. When you say next one, it's another... We've had a few of the key ideas behind LightGBM, which also, the etymology of that Light Gradient-Boosting Machine is what GBM ...
LightGBM 在 XGBoost 的基础上，针对大数据和高维特征进行了优化。它采用了直方图离散化技术，将特征值分桶，从而减少计算复杂度。LightGBM 还使用了 GOSS（Gradient-Based One-Side Sampling）权重采样方法，通过梯度阈值来减少训练样本量，同时保持算法的鲁棒性。CatBoost 是专门针对分类特征优化的算法。它采用...
Gradient-based One-Side Sampling, or GOSS for short, is a modification to the gradient boosting method that focuses attention on those training examples that result in a larger gradient, in turn speeding up learning and reducing the computational complexity of the method. With GOSS, we exclude ...
To tackle this problem, we propose two novel techniques: \emph{Gradient-based One-Side Sampling} (GOSS) and \emph{Exclusive Feature Bundling} (EFB). With GOSS, we exclude a significant proportion of data instances with small gradients, and only use the rest to estimate the...
If you like, you can change this to the random forest algorithm, `dart` — Dropouts meet Multiple Additive Regression Trees, or `goss` — Gradient-based One-Side Sampling. import lightgbm as lgb lgb_train = lgb.Dataset(X_train, y_train) ...