Find out how to GPU-Speed up Model Training with CUDA-X Data Science

The three popular gradient-boosting frameworks—XGBoost, LightGBM, and CatBoost—primarily differ of their tree growth strategies, methods for handling categorical features, and overall optimization techniques. These differences end in trade-offs between speed, accuracy, and ease of use.

XGBoost 

XGBoost (eXtreme Gradient Boosting) builds trees using a level-wise (or depth-wise) growth strategy. This implies it splits all possible nodes at the present depth before moving to the subsequent level, leading to balanced trees. While this approach is thorough and helps prevent overfitting through regularization, it might probably be computationally expensive when run on CPUs. Because of the parallelizability of the tree expansion, GPUs can massively reduce training time of XGBoost while being robust.

• Key feature: Level-wise tree growth for balanced trees and robust regularization.
• Best for: Situations where accuracy, regularization and speed of iterations (on GPUs)  are paramount.

LightGBM 

LightGBM (Light Gradient Boosting Machine) was designed for speed and efficiency at the associated fee of robustness. It uses a leaf-wise growth strategy, where it exclusively splits the leaf node that can yield the most important reduction in loss. This approach converges much faster than the level-wise method, making LightGBM extremely efficient. Nonetheless, this will result in deep, unbalanced trees, which run a better risk of overfitting on certain datasets without proper regularization.

• Key feature: Leaf-wise tree growth for optimum speed. It also uses advanced techniques like gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) to further boost performance.
• Best for: First iterations to determine a baseline on large datasets where memory efficiency is critical.

CatBoost 

The most important advantage of CatBoost (Categorical Boosting) is its sophisticated, native handling of categorical features. Standard techniques like goal encoding often suffer from goal leakage, where information from the goal variable improperly influences the feature encoding. CatBoost solves this with ordered boosting, a permutation-based strategy that calculates encodings using only the goal values from previous examples in an ordered sequence. 

Moreover, CatBoost builds symmetric (oblivious) trees, where all nodes at the identical level use the identical splitting criterion, which acts as a type of regularization and quickens execution on CPUs.

• Key feature: Superior handling of categorical data using ordered boosting to forestall goal leakage.
• Best for: Datasets with either a lot of categorical features or features with large cardinality, where ease of use and out-of-the-box performance are desired.

While increasingly faster GPU accelerations can be found in native libraries for training these models, the cuML Forest Inference Library (FIL) can dramatically speed up the inference speed on any tree-based model that may be converted to Treelite equivalent to XGBoost, RandomForest models from Scikit-Learn and cuML, LightGBM, and more. To try FIL capabilities, download cuML (a part of RAPIDS).

Do more features at all times result in a greater model? 

A standard mistake is assuming that more features at all times result in a greater model. In point of fact, because the feature count rises, validation loss eventually plateaus. Adding more columns beyond a certain point rarely improves performance and may even introduce noise.

The hot button is to seek out the “sweet spot.” You may do that by plotting validation loss against the variety of features used. In a real-world scenario, you’d first train a baseline model (like a Random Forest) on all features to get an initial rating of feature importance. You then use this rating to plot the validation loss as you incrementally add an important features, similar to in the instance below.

The next Python snippet puts this idea into practice. It first generates a large synthetic dataset (10,000 samples, 5,000 features) where only a small subset of features is definitely informative. It then evaluates the model’s performance by incrementally adding an important features in batches.

# Generate synthetic data with informative, redundant, and noise features
X, y, feature_names, feature_types = generate_synthetic_data(
n_samples=10000,
       n_features=5000,
       n_informative=100,
       n_redundant=200,
       n_repeated=50
)

# Progressive feature evaluation. Evaluating 100 features at a time, and compute validation loss because the feature set becomes larger
n_features_list, val_losses, feature_counts = progressive_feature_evaluation(
        X, y, feature_names, feature_types, step_size=100, max_features=2000
    )

# Find optimal variety of features (elbow method)
improvements = np.diff(val_losses)
improvement_changes = np.diff(improvements)
elbow_idx = np.argmax(improvement_changes) + 1

print(f"nElbow point detected at {n_features_list[elbow_idx]} features")
print(f"Validation loss at elbow: {val_losses[elbow_idx]:.4f}")

# Plot results
plot_results(n_features_list, val_losses, feature_types, feature_names)

This code example uses synthetic data with a known rating. To use this approach to a real-world problem:

1. Get a baseline rating: Train a preliminary model, like a Random Forest or LightGBM, in your entire feature set to generate an initial feature importance rating for each column.
2. Plot the curve: Use that rating to incrementally add features—from most to least vital—and plot the validation loss at each step.

This method means that you can visually discover the purpose of diminishing returns and choose probably the most efficient feature set in your final model.

Why use the Forest Inference Library to supercharge inference?

While training gets a whole lot of attention, inference speed is what matters in production. For giant models like XGBoost, this will change into a bottleneck. The FIL, available in cuML, solves this problem by delivering lightning-fast prediction speeds.

The workflow is easy: Train your XGBoost, LightGBM, or other gradient-boosted models using their native GPU acceleration, then load and serve them with FIL. This means that you can achieve massive inference speedups—as much as 150x and 190x over native scikit-learn for batch size of 1 and enormous batch size inference respectively—even on hardware separate out of your training environment. For a deep dive, take a look at Supercharge Tree-Based Model Inference with Forest Inference Library in NVIDIA cuML. 

Model interpretability: Gaining insights beyond accuracy

One in all the best strengths of tree-based models is their transparency. Feature importance evaluation helps engineers understand which variables drive predictions. To take this a step further, you possibly can run “random feature” experiments to determine a baseline for importance.

The concept is to inject random noise features into your dataset before training. Whenever you later compute feature importances using a tool like SHAP (SHapley Additive exPlanations), any of your real features which are no more vital than the random noise may be safely disregarded. This method provides a sturdy strategy to filter out uninformative features.

# Generate random noise features
X_noise = np.random.randn(n_samples, n_noise)

# Mix informative and noise features
X = np.column_stack([X, X_noise])

This type of interpretability is invaluable for validating model decisions and uncovering recent insights for continuous process improvement.

Start with tree-based model training

Tree-based models, especially when accelerated by GPU-optimized libraries like cuML, offer a perfect balance of accuracy, speed, and interpretability for manufacturing and operations data science. By fastidiously choosing the suitable model and leveraging the newest inference optimizations, engineering teams can rapidly iterate and deploy high-performing solutions on the factory floor.

Learn more about cuML and scaling up XGBoost. In case you’re recent to accelerated data science, take a look at the hands-on workshops, Speed up Data Science Workflows with Zero Code Changes and Accelerating End-to-End Data Science Workflows.