Why XGBoost2WW?

XGBoost2WW lets you apply WeightWatcher-style spectral diagnostics to XGBoost models.

XGBoost models don’t have traditional neural network weight matrices — so you can’t directly run tools like WeightWatcher on them.
XGBoost2WW bridges that gap by converting a trained XGBoost model into structured matrices (W1/W2/W7/W8/W9) derived from out-of-fold margin increments along the boosting trajectory.

These matrices behave like neural weight matrices, so you can analyze them with WeightWatcher.

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Why would a production ML engineer care?

Because traditional metrics (accuracy, AUC, logloss) often look fine right up until a model fails in production.

Spectral diagnostics can help detect:

• Overfitting that standard validation doesn’t reveal
• Correlation traps in boosted trees
• Excessive memorization
• Unstable training dynamics
• Data leakage patterns
• Models that are brittle to distribution shift

In short:

XGBoost2WW gives you a structural diagnostic signal — not just a performance metric.

That means you can:

• Compare model candidates beyond accuracy
• Detect problematic models before deployment
• Monitor structural drift over time
• Add an extra safety layer to your MLOps pipeline

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If you deploy XGBoost models in production,
XGBoost2WW gives you a new lens to inspect them.

xgboost2ww

Convert XGBoost boosting dynamics into WeightWatcher-style operators (W1/W2/W7/W8/W9).

Install

Development install:

pip install -e .
pip install weightwatcher torch

Minimal runtime install:

pip install xgboost2ww
pip install weightwatcher

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Google Colab Notebooks

Single Good Model

• High test and training accuracy, good WW metrics SingleGoodModelWWXGBoost2WW.ipynb

Realistic End-to-End Example

• Interpreting α and traps in a realistic, non-trivial setting XGBoost2WWAdultIncomeExample.ipynb

Stress Test Across 100 Random Models

• Trains 100 random models, analyzes weightwatcher alpha vs test accuracy GoodModelsXGBoost2WW.ipynb

Poorly Trained Credit Model

• Small data set, hard to get high test accuracy, shows high alpha PoorlyTrainedCreditModel.ipynb

Diagnostic Example

• Overly simple model where the training data is strongly overfit XGBoost2WWDiagnosticExample.ipynb

SpamBase Alpha=2 Targeted Sweep

• Targeted hyperparameter sweep to maximize validation accuracy near α≈2.0 SpamBase_Hyperparameter_Sweep_Alpha2_Targeted.ipynb

Random100 Long-Run Alpha Tracking

• Long-running Random100 catalog training with Google Drive checkpoints and restart/resume support XGBWW_Catalog_Random100_XGBoost_Accuracy_LongRun_AlphaTracking.ipynb

Quickstart (compute_matrices)

import numpy as np
import xgboost as xgb

from xgboost2ww import compute_matrices

rng = np.random.default_rng(0)
X = rng.normal(size=(300, 12)).astype(np.float32)
logits = 1.5 * X[:, 0] - 0.8 * X[:, 1] + 0.3 * rng.normal(size=300)
y = (logits > 0).astype(np.int32)

dtrain = xgb.DMatrix(X, label=y)

params = {
    "objective": "binary:logistic",
    "eval_metric": "logloss",
    "max_depth": 3,
    "eta": 0.1,
    "subsample": 1.0,
    "colsample_bytree": 1.0,
    "seed": 0,
    "verbosity": 0,
}
rounds = 40
bst = xgb.train(params, dtrain, num_boost_round=rounds)

# Reproducibility knobs for fold training inside compute_matrices / convert
train_params = params
num_boost_round = rounds

mats = compute_matrices(
    bst,
    X,
    y,
    nfolds=5,
    t_points=40,
    random_state=0,
    train_params=train_params,
    num_boost_round=num_boost_round,
)

W9 = mats.W9
print(W9.shape)

Quickstart (convert + WeightWatcher)

import weightwatcher as ww

from xgboost2ww import convert

layer = convert(
    bst,
    X,
    y,
    W="W9",
    return_type="torch",
    nfolds=5,
    t_points=40,
    random_state=0,
    train_params=train_params,
    num_boost_round=num_boost_round,
)

watcher = ww.WeightWatcher(model=layer)
details_df = watcher.analyze(randomize=True, plot=False)

alpha = details_df["alpha"].iloc[0]
rand_num_spikes = details_df["rand_num_spikes"].iloc[0]
print({"alpha": alpha, "rand_num_spikes": rand_num_spikes})

For initial evaluation, you do not need detX=True. If you want determinant-based diagnostics, you can pass detX=True.

What the Diagnostics Look Like

Below is an example of a WeightWatcher spectral analysis applied to an XGBoost model via xgboost2ww.

In this example:

• (Left) The power-law fit produces an α value near 2
• (Right) The ERG detX condition is pretty good (red and purple lines are close)
• (not shown0 No significant traps are detected

But unlike many Neural Networksm the full empirical spectral density (ESD) shows very-heavy-tailed structure. This indicates that the training data was very easy to memorize. That's OK. Even expected.

This is what a structurally healthy model looks like.

When α drifts upward or traps appear, it is often a signal of:

• Overfitting
• Correlation traps
• Memorization
• Instability in training
• Data leakage
• Structural brittleness

Matrix defaults and stability

• convert(...) now defaults to W="W1" (instead of W="W7").
• W1 is the recommended non-experimental default.
• All other W variants (W2, W7, W8, W9) are currently experimental.

Matrix definitions at a glance

• W8: legacy practical surrogate based on weighted/centered W7.
• W9: canonical regularizer-whitened Fisher OOF trajectory matrix.

For binary classification, with raw OOF increments dF_oof and final OOF margin m_final:

p = sigmoid(m_final)
h = clip(p * (1 - p), eps, None)
A = weighted_center_cols(dF_oof, h)
gamma_diag[j] = lambda * sum_{trees in endpoint block j}(leaf_value^2)
W9 = diag(sqrt(h)) · A · diag(gamma_diag^{-1/2})

W9 v1 uses the local-quadratic L2 regularizer mass (lambda) and intentionally ignores the non-smooth L1 (alpha) term.

Notes / limitations

• Binary classification is the default workflow.
• Multiclass requires setting multiclass explicitly (supported modes: "per_class", "stack", "avg").
• convert(..., multiclass="per_class", return_type="torch") is unsupported and raises; for multiclass per-class output, use return_type="numpy".
• torch is optional unless you need convert(..., return_type="torch").