As highlighted in the chapter introduction, training gradient boosting models on large datasets can be computationally intensive. A significant portion of this cost comes from iterating through all data instances to evaluate potential splits when building each tree. Gradient-based One-Side Sampling (GOSS) is a core innovation within LightGBM designed to accelerate this process by intelligently reducing the number of data instances used for finding the best splits.

The fundamental insight behind GOSS is that not all training instances contribute equally to the learning process at each boosting iteration. Recall that gradient boosting works by sequentially fitting weak learners (typically decision trees) to the negative gradients of the loss function concerning the current model's predictions. The gradient for an instance essentially represents the residual error or how "wrong" the current ensemble is for that specific instance.

Instances with large gradients are those for which the model is currently making significant errors. These instances are, in a sense, "under-trained" and provide substantial information about how to improve the model in the next iteration. Conversely, instances with small gradients are already well-predicted by the current ensemble; further refinement based heavily on these instances might lead to overfitting on minor variations and yield diminishing returns in terms of overall model improvement, while still incurring computational cost.

GOSS leverages this observation by proposing a non-uniform sampling strategy. Instead of treating all instances equally or using simple random sampling, GOSS prioritizes instances with larger gradients while maintaining a representative sample of instances with smaller gradients. This achieves computational savings without drastically altering the data distribution required for effective learning.

The GOSS Mechanism

The GOSS procedure can be broken down into the following steps during the training of each tree:

Calculate Gradients: Compute the negative gradients of the loss function for all training instances based on the predictions of the ensemble built so far.
Sort by Gradient Magnitude: Sort the instances in descending order based on the absolute value of their gradients: $|g_i|$ .
Select High-Gradient Instances: Keep the top $a \times 100\%$ of instances with the largest absolute gradients. Let this set be denoted by $A$ . These are the instances the model is currently struggling most with. The parameter $a$ is typically referred to as top_rate in LightGBM.
Sample Low-Gradient Instances: From the remaining $(1-a) \times 100\%$ of instances (those with smaller gradients), randomly sample a fraction $b$ . This means selecting $b \times (1-a) \times N$ instances, where $N$ is the total number of instances. Let this set be denoted by $B$ . The parameter $b$ is referred to as other_rate in LightGBM.
Combine and Amplify: Combine the two sets of instances: $A \cup B$ . This smaller, combined dataset is used for finding the optimal splits in the current tree.
Amplify Small Gradients: To ensure that the contribution of the low-gradient instances (set $B$ ) is not underestimated due to down-sampling, their gradients are amplified. Specifically, the gradients $g_i$ for instances $i \in B$ are multiplied by a constant factor: $\frac{1-a}{b}$ . This amplification serves to maintain an approximately unbiased estimate of the overall gradient distribution. If we didn't amplify, the contribution of the frequently well-predicted instances would be systematically underestimated when calculating split gains.

Why Amplification Matters

The information gain calculation for finding the best split in a decision tree relies heavily on the sum of gradients and hessians (or just gradients, depending on the specific objective) within the potential child nodes. By sampling the low-gradient instances (set $B$ ) with a probability $b$ , we reduce their count. To compensate for this reduction and ensure the expected value of the sum of gradients in set $B$ approximates the sum over the original low-gradient population it represents, we need to scale the sampled gradients upwards. The factor $\frac{1-a}{b}$ precisely achieves this, scaling the sampled gradients $g_i$ for $i \in B$ so that their sum statistically represents the sum over the entire pool of $(1-a) \times N$ low-gradient instances they were sampled from.

Visualizing GOSS

Imagine the distribution of absolute gradient magnitudes across your dataset. GOSS effectively keeps all instances above a certain threshold (the top 'a' fraction) and then randomly samples from the instances below that threshold, scaling their influence back up.

Illustration of GOSS selection. Instances with high gradients (red squares) are always kept. Instances with low gradients (gray bars) are randomly sampled (blue circles), and their gradients are amplified during gain calculation. Instances represented by gray bars without blue circles are discarded for this tree's split finding.

Advantages of GOSS

Efficiency: Reduces the amount of data scanned for finding splits, leading to significant speedups in training time, particularly noticeable on large datasets.
Accuracy: By focusing on informative instances and properly weighting the sampled low-gradient instances, GOSS aims to find splits that are nearly as good as those found using the full dataset, thus preserving model accuracy. It often performs better than naive random sampling, which might discard too many important high-gradient instances by chance.

GOSS vs. Stochastic Gradient Boosting

It's important to distinguish GOSS from the subsampling techniques used in traditional Stochastic Gradient Boosting (as implemented in Scikit-learn or standard XGBoost). Stochastic Gradient Boosting typically performs uniform random sampling of instances (row subsampling) before building each tree. GOSS, in contrast, performs biased sampling based on gradient magnitudes. It assumes that instances with smaller gradients are less critical for defining the optimal split structure and can be sampled more sparsely, provided their influence is correctly scaled.

Practical Usage in LightGBM

When using LightGBM, GOSS is enabled by default as part of the 'boosting_type' (or 'boosting') parameter set to 'gbdt'. The specific behavior is controlled by:

top_rate ( $a$ ): The fraction of instances with the largest gradients to keep. Default is often 0.2. Increasing this retains more high-gradient data but reduces the speed gain.
other_rate ( $b$ ): The fraction of the remaining (low-gradient) instances to sample. Default is often 0.1. Increasing this uses more low-gradient data, potentially improving accuracy slightly at the cost of speed.

These parameters usually don't require extensive tuning but can be adjusted if you suspect GOSS is negatively impacting accuracy on a specific problem or if you need to maximize training speed further.

In summary, Gradient-based One-Side Sampling is a clever technique that significantly contributes to LightGBM's efficiency by reducing the computational burden of finding splits, focusing computational effort on the data points that matter most for improving the model at each step.