PyTorch 2.0 introduced torch.compile(), a one-line speedup for PyTorch models. It compiles your model’s computation graph using TorchDynamo and TorchInductor, fusing operations and generating optimised kernels for your hardware. On NVIDIA GPUs, compile mode typically delivers 10–50% speedup with no changes to model architecture or training loop. This guide covers how to use it, what … Read more
PyTorch and JAX both dominate modern deep learning research, but they make fundamentally different design choices. PyTorch is object-oriented, eager by default, and built around mutable tensors and a familiar Python programming model. JAX is functional, pure, and built around function transformations — you write plain Python functions and apply jit, grad, vmap, and pmap … Read more
TabNet is a deep learning architecture designed specifically for tabular data. It uses sequential attention to select which features to focus on at each decision step — essentially learning feature selection and transformation jointly rather than relying on hand-crafted engineering. XGBoost is gradient boosted trees, the dominant tabular baseline for the past decade. The comparison … Read more
Saving and loading XGBoost models sounds trivial — and mostly it is — but there are enough format options, version compatibility nuances, and post-load gotchas (particularly around early stopping) to make it worth covering carefully. This guide covers every serialisation format XGBoost supports, when to use each, and what to watch out for. Format Overview … Read more
Wrapping XGBoost in a scikit-learn pipeline solves a problem that bites almost every ML project eventually: training-serving skew. When preprocessing steps live outside the model, it is easy for inference code to apply them slightly differently — wrong scaler, missing imputer, column order mismatch — producing silent errors that corrupt predictions without raising exceptions. A … Read more
Cross-validation gives you a reliable estimate of how an XGBoost model will perform on unseen data. A single train/test split is noisy — performance varies significantly depending on which samples happen to end up in the test set. Cross-validation reduces that variance by averaging over multiple splits. This guide covers how to do it correctly … Read more
XGBoost handles multiclass classification with the same API as binary classification — just change the objective and let XGBoost figure out the rest. The model trains one set of trees per class (one-vs-all internally), outputs a probability for each class, and predicts the class with the highest probability. This guide covers the full workflow: training, … Read more
Imbalanced classification — fraud detection, medical diagnosis, churn prediction, rare event detection — is where default model settings fail most predictably. A model that predicts the majority class for every sample achieves 99% accuracy on a 1:99 dataset while being completely useless. LightGBM has several built-in mechanisms for imbalanced data, and combining them with the … Read more
XGBoost is not a time series model — it has no built-in understanding of temporal order, seasonality, or autocorrelation. But it becomes a powerful time series forecaster once you add that structure through feature engineering. The approach is to transform a forecasting problem into a supervised regression problem: create lag features, rolling statistics, and calendar … Read more
Feature importance scores built into XGBoost — gain, weight, cover — tell you which features the model used, but not how or in which direction. SHAP values do both: they assign each feature a contribution to each individual prediction, with sign indicating direction and magnitude indicating impact. The result is a local explanation you can … Read more
