CatBoost vs XGBoost: which one to use for tabular data, and what matters more

If your data lives in tables (customers, transactions, sensor readings, lab measurements), gradient-boosted trees are usually the first model worth trying. And sooner or later you'll ask the question: CatBoost or XGBoost?

I've used both on real projects, including a geological classification system where we compared them side by side. My honest answer: both are excellent, the differences are real but smaller than people think, and the way you validate your model matters far more than which library you pick.

CatBoost vs XGBoost: the short version

Pick CatBoost when you have many categorical columns (product type, region, customer segment, sensor ID) and you want good results with little tuning.
Pick XGBoost when your features are mostly numeric, you want fine control over the trees, or you need the widest ecosystem and deployment options.
Try both when the model matters. It's cheap.

Categorical features: the biggest practical difference

This is where CatBoost earns its name. You can pass categorical columns directly, and CatBoost encodes them internally using target statistics computed in an ordered way: each row's encoding only uses rows that came "before" it in a random permutation. That reduces the target leakage you get when you naively replace a category with its average label.

XGBoost traditionally needs you to encode categories yourself: one-hot encoding, ordinal codes or target encoding. Recent versions added native categorical support, but you have to enable it and use the right data types, and it behaves differently from CatBoost's approach. If you do target encoding by hand, you have to do it inside each cross-validation fold, or you'll leak the label into your features and get scores that collapse in production.

For data with a few low-cardinality categories, this barely matters. For data with high-cardinality categories (thousands of product IDs or postcodes), CatBoost often saves you real work.

Defaults and tuning effort

CatBoost's defaults are good. On many datasets, an untuned CatBoost model is close to its tuned version. That's useful when you need a solid baseline quickly or when the person maintaining the model isn't an ML specialist.

XGBoost rewards tuning more. Learning rate, tree depth, minimum child weight, subsampling and regularisation all matter, and the defaults are a starting point rather than an answer. It isn't hard, but plan for a proper search, ideally with early stopping on a validation fold.

Tree structure

CatBoost builds symmetric (oblivious) trees by default: at each depth, every node uses the same split. These trees are fast at prediction time and act as a form of regularisation, which partly explains why the defaults hold up well.

XGBoost grows trees depth-wise by default and can also grow them loss-guided, closer to how LightGBM grows leaf-wise. That flexibility can fit complex interactions better, and can also overfit faster if you don't control it.

Speed, GPUs and overfitting

Both libraries support GPU training and both are fast enough for most business datasets on a laptop. Speed comparisons depend heavily on the data shape, the settings and the hardware, so I don't trust generic benchmarks. Time it on your own data.

For overfitting, both give you the tools you need: early stopping, depth limits, learning rate, row and column subsampling, and L2 regularisation. Use early stopping on a held-out fold in every case. It's the single setting that saves the most trouble.

Don't forget LightGBM

LightGBM is the third common option. It's often the fastest on large datasets because of its histogram-based, leaf-wise growth, and it has its own categorical handling. In practice the three libraries tend to land close to each other on the same well-prepared data. If one is dramatically better than the others, check your pipeline before celebrating.

Validation matters more than the library

Here's the part most comparisons skip. The biggest source of misleading results in tabular ML isn't the library. It's leakage between training and test data.

If your rows come in groups (several measurements per patient, per customer, per machine, per borehole), a random train/test split puts rows from the same group on both sides. The model learns to recognise the group instead of the pattern, and your test score looks great. Then a genuinely new patient, customer or borehole arrives, and the score drops.

The fix is grouped cross-validation: keep all rows of a group in the same fold. The strictest version is leave-one-group-out, where each fold holds out exactly one group. It's slower, but it answers the question you actually care about: how well does the model do on something it has never seen?

For time-dependent data, the same idea applies with time: train on the past, test on the future. I cover that in demand forecasting for small businesses.

A real example: geological layers from CPT data

In the CPT Dashboard project, a five-person data science project at Hasselt University with data from VITO, the task was to predict which of 15 geological layers sits at every depth of a cone penetration test (CPT). The dataset had about 184,000 labelled measurements.

Those measurements come in soundings: one CPT gives hundreds of rows, all from the same spot in the ground. Neighbouring rows are almost identical. A random split would have put rows from the same sounding in training and test, and the scores would have been meaningless.

So every model was validated by holding out entire soundings, leave-one-CPT-out across 238 soundings. We compared CatBoost, XGBoost, Random Forest and LightGBM against sequence models (LSTM, hidden Markov models and CRFs), all on that same strict validation. The trees worked on depth, cone resistance, friction ratio, soil-behaviour indices and position.

The model was only half the answer. Raw predictions jump between layers in ways no geologist would accept, so a post-processing step uses Viterbi dynamic programming to enforce the order in which layers appear, merges short runs and fixes sandwiched layers. The dashboard then shows the three most plausible interpretations with confidence scores. Users can also train a new CatBoost, XGBoost or Random Forest model from the browser and switch between versions.

The lesson I took from it: once validation was honest, the choice between gradient-boosting libraries was a detail. Getting the validation and the domain rules right was the real work.

How I'd choose today

Set up grouped or time-based validation first. Decide what "unseen" means for your problem.
Train CatBoost with defaults as a strong baseline, especially with categorical data.
Train XGBoost or LightGBM with early stopping and a modest tuning budget.
Compare on the same folds, and look at errors by group, not just the average score.
Pick the simplest model that holds up, and spend the saved time on features and on what happens after the prediction.

Working together

I'm Driss Briksine, a software and ML engineer with a master's in statistics and data science from Hasselt University. I build ML models and the production software around them. See the CPT Dashboard case study and my other work, or read how I'd approach choosing what to automate first.

If you have tabular data and a prediction you need to trust, send me a short brief.