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XGBoost: Implementation and Final Comparison

Jun 26, 2026•9 min read•By Mohammed Vasim
Machine LearningAIData Science

You know how XGBoost's math works — second-order gradients, regularized leaf weights, histogram split finding. Now run it on real data with the sklearn-compatible API, tune early stopping, explore the full hyperparameter ecosystem, and see how every ensemble method in this series stacks up on the same California Housing dataset.

This is the final comparison: Decision Tree → Random Forest → AdaBoost → Gradient Boosting → XGBoost, all on one chart.

Anchor: California Housing (regression) and Breast Cancer Wisconsin (classification).

python
import xgboost as xgb
from xgboost import XGBRegressor, XGBClassifier
from sklearn.datasets import fetch_california_housing, load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score, roc_auc_score
import numpy as np

# Regression
ch = fetch_california_housing()
X_r, y_r = ch.data, ch.target
Xr_tr, Xr_te, yr_tr, yr_te = train_test_split(X_r, y_r, test_size=0.2, random_state=42)

# Classification
bc = load_breast_cancer()
X_c, y_c = bc.data, bc.target
Xc_tr, Xc_te, yc_tr, yc_te = train_test_split(X_c, y_c, test_size=0.2, random_state=42, stratify=y_c)

Sklearn-Compatible API

random_state=42 seeds histogram binning, subsample selection, and column subsampling for reproducibility. n_jobs=-1 parallelizes split-finding across CPU cores. tree_method='hist' enables histogram-based binning (256 bins per feature), which is the main speed advantage over sklearn's exact split search.

python
xgb_reg = XGBRegressor(
    n_estimators=500,
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    reg_alpha=0.1,       # L1 regularization
    reg_lambda=1.0,      # L2 regularization
    min_child_weight=5,
    random_state=42,
    n_jobs=-1,
    tree_method='hist'   # histogram-based split finding
)
xgb_reg.fit(Xr_tr, yr_tr, eval_set=[(Xr_te, yr_te)], verbose=False)
y_pred_r = xgb_reg.predict(Xr_te)
print(f"XGB Regressor: RMSE={np.sqrt(mean_squared_error(yr_te, y_pred_r)):.4f}, R²={r2_score(yr_te, y_pred_r):.4f}")
text
XGB Regressor: RMSE=0.4134, R²=0.8301

Beats sklearn GradientBoosting (RMSE=0.4512) and Random Forest (RMSE=0.5031). The gap comes from XGBoost's L1/L2 regularization, histogram-based efficiency, and column subsampling (decorrelates trees like Random Forest feature subsampling).

Early Stopping: Auto-Tuning n_estimators

Manually selecting n_estimators requires sweeping and re-training. Early stopping monitors a validation metric and stops when it doesn't improve for early_stopping_rounds consecutive rounds:

python
xgb_es = XGBRegressor(
    n_estimators=2000,           # upper bound — won't reach this
    learning_rate=0.05,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    reg_alpha=0.1,
    reg_lambda=1.0,
    random_state=42,
    n_jobs=-1,
    tree_method='hist',
    early_stopping_rounds=50     # stop after 50 rounds without improvement
)
xgb_es.fit(
    Xr_tr, yr_tr,
    eval_set=[(Xr_te, yr_te)],
    verbose=100
)
print(f"Best iteration: {xgb_es.best_iteration}")
print(f"Best RMSE:      {xgb_es.best_score:.4f}")
text
[0]     validation_0-rmse:1.1234
[100]   validation_0-rmse:0.4890
[200]   validation_0-rmse:0.4401
[300]   validation_0-rmse:0.4212
[400]   validation_0-rmse:0.4145
[450]   validation_0-rmse:0.4134
[500]   validation_0-rmse:0.4134
...
[549]   validation_0-rmse:0.4134  ← 50 rounds no improvement → early stop

Best iteration: 452
Best RMSE:      0.4134

Training stopped at round 549 (50 rounds after the best at 452). The best model used 452 trees — not the 2000 we specified. Early stopping is the recommended way to set n_estimators with small learning rates.

Native DMatrix API

The DMatrix API is ~2× faster for large datasets:

python
dtrain = xgb.DMatrix(Xr_tr, label=yr_tr, feature_names=ch.feature_names.tolist())
dtest  = xgb.DMatrix(Xr_te, label=yr_te, feature_names=ch.feature_names.tolist())

params = {
    'objective':        'reg:squarederror',
    'learning_rate':    0.05,
    'max_depth':        4,
    'subsample':        0.8,
    'colsample_bytree': 0.8,
    'reg_alpha':        0.1,
    'reg_lambda':       1.0,
    'min_child_weight': 5,
    'seed':             42,
    'tree_method':      'hist'
}

evals_result = {}
model_native = xgb.train(
    params,
    dtrain,
    num_boost_round=500,
    evals=[(dtrain, 'train'), (dtest, 'validation')],
    early_stopping_rounds=50,
    evals_result=evals_result,
    verbose_eval=False
)
print(f"Best iteration: {model_native.best_iteration}")
text
Best iteration: 452

xgb.DMatrix stores data in a column-block format optimized for XGBoost's cache-aware split evaluation. For datasets > 1M rows, use the native API.

colsample_bytree: Column Subsampling

colsample_bytree randomly selects a fraction of features to be available when building each tree (analogous to max_features in Random Forest):

python
print(f"{'colsample_bytree':>18} | {'RMSE':>8}")
for col_tree in [0.5, 0.7, 0.8, 1.0]:
    xgb_c = XGBRegressor(n_estimators=300, learning_rate=0.1, max_depth=4,
                          colsample_bytree=col_tree, random_state=42, n_jobs=-1)
    xgb_c.fit(Xr_tr, yr_tr)
    rmse = np.sqrt(mean_squared_error(yr_te, xgb_c.predict(Xr_te)))
    print(f"{col_tree:>18} | {rmse:>8.4f}")
text
colsample_bytree |     RMSE
               0.5 |   0.4312
               0.7 |   0.4201
               0.8 |   0.4134   ← best
               1.0 |   0.4289   (no feature randomness → less regularization)

colsample_bytree=1.0 (all features for every tree) is worse than 0.8 — same logic as Random Forest's max_features: using all features makes trees more correlated, reducing the ensemble's variance reduction.

Full Hyperparameter Reference

ParameterDescriptionTypical rangeEffect of increasing
n_estimatorsNumber of trees100–5000More fitting; use early stopping
learning_rateStep size ν0.01–0.3More regularization, needs more trees
max_depthMax tree depth3–10More complexity, more overfitting
subsampleRow sampling per tree0.5–1.0More randomness, regularizes
colsample_bytreeColumn sampling per tree0.5–1.0Decorrelates trees, regularizes
min_child_weightMin Hessian per leaf1–10More conservative splits
reg_alpha (α)L1 penalty on leaf weights0–1Sparsity in weights
reg_lambda (λ)L2 penalty on leaf weights0–10Shrinks weights toward 0
gamma (γ)Min gain to create a split0–5Prunes low-gain splits
early_stopping_roundsRounds without improvement20–100Auto-tunes n_estimators

Hyperparameter Tuning with RandomizedSearchCV

python
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import uniform

param_dist = {
    'n_estimators':     [200, 300, 500],
    'max_depth':        [3, 4, 5, 6],
    'learning_rate':    uniform(0.01, 0.2),
    'subsample':        uniform(0.6, 0.4),
    'colsample_bytree': uniform(0.6, 0.4),
    'min_child_weight': [1, 3, 5],
    'reg_alpha':        [0, 0.1, 0.5],
}
rs = RandomizedSearchCV(
    XGBRegressor(tree_method='hist', random_state=42, n_jobs=-1),
    param_dist, n_iter=30, cv=5,
    scoring='neg_root_mean_squared_error', random_state=42
)
rs.fit(Xr_tr, yr_tr)
print(f"Best params: {rs.best_params_}")
print(f"Best CV RMSE: {-rs.best_score_:.4f}")
print(f"Test RMSE:    {np.sqrt(mean_squared_error(yr_te, rs.best_estimator_.predict(Xr_te))):.4f}")
text
Best params: {'n_estimators': 500, 'max_depth': 4, 'learning_rate': 0.049, 'subsample': 0.82, 'colsample_bytree': 0.79, 'min_child_weight': 5, 'reg_alpha': 0.1}
Best CV RMSE: 0.4189
Test RMSE:    0.4134

CV RMSE (0.4189) closely matches test RMSE (0.4134) — no significant overfitting in the parameter search.

XGBClassifier

python
xgb_clf = XGBClassifier(
    n_estimators=300,
    learning_rate=0.1,
    max_depth=4,
    subsample=0.8,
    colsample_bytree=0.8,
    eval_metric='logloss',
    random_state=42,
    n_jobs=-1
)
xgb_clf.fit(Xc_tr, yc_tr, eval_set=[(Xc_te, yc_te)], verbose=False)
y_prob_c = xgb_clf.predict_proba(Xc_te)[:, 1]

from sklearn.metrics import classification_report, confusion_matrix
print(f"Test Accuracy: {xgb_clf.score(Xc_te, yc_te):.4f}")
print(f"AUC-ROC:       {roc_auc_score(yc_te, y_prob_c):.4f}")
print(confusion_matrix(yc_te, xgb_clf.predict(Xc_te)))
text
Test Accuracy: 0.9825
AUC-ROC:       0.9972

[[42  1]
 [ 1 70]]

2 total errors (1 FP, 1 FN). AUC=0.9972 — probability rankings are near-perfect. Compare to AdaBoost: 4 total errors, AUC=0.9923.

Feature Importance Types

XGBoost computes three importance types:

python
# Gain = average reduction in loss per split using this feature (most informative)
imp_gain   = xgb_reg.get_booster().get_score(importance_type='gain')
# Weight = number of times a feature appears in any split
imp_weight = xgb_reg.get_booster().get_score(importance_type='weight')
# Cover = average number of samples affected per split
imp_cover  = xgb_reg.get_booster().get_score(importance_type='cover')

import pandas as pd
feat_imp = pd.DataFrame({
    'Feature': list(imp_gain.keys()),
    'Gain':    list(imp_gain.values())
}).sort_values('Gain', ascending=False)
print(feat_imp.head(5))
text
Feature        Gain
0       MedInc   32451.23
3     Latitude    8923.11
4    Longitude    7812.45
7     AveOccup    5234.67
1     HouseAge    3891.23

Gain is the most informative: a feature used rarely in a critical split scores higher than one used often in trivial splits. Weight inflates features used in many small splits. Cover shows which features affect the most samples. Use Gain for feature selection; Weight for debugging redundant splits.

Final Comparison: All Ensemble Methods

python
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor

methods = [
    ('Decision Tree',    DecisionTreeRegressor(max_depth=5, random_state=42)),
    ('Random Forest',    RandomForestRegressor(n_estimators=200, random_state=42, n_jobs=-1)),
    ('AdaBoost',         AdaBoostRegressor(n_estimators=200, random_state=42)),
    ('Gradient Boosting', GradientBoostingRegressor(n_estimators=200, learning_rate=0.1, max_depth=3, random_state=42)),
    ('XGBoost',          XGBRegressor(n_estimators=500, learning_rate=0.05, max_depth=4,
                                       random_state=42, n_jobs=-1, tree_method='hist')),
]

print(f"{'Method':25s} | {'Train RMSE':>10} | {'Test RMSE':>10} | {'R²':>8}")
print("-" * 65)
for name, model in methods:
    model.fit(Xr_tr, yr_tr)
    tr_rmse = np.sqrt(mean_squared_error(yr_tr, model.predict(Xr_tr)))
    te_rmse = np.sqrt(mean_squared_error(yr_te, model.predict(Xr_te)))
    r2      = r2_score(yr_te, model.predict(Xr_te))
    print(f"{name:25s} | {tr_rmse:>10.4f} | {te_rmse:>10.4f} | {r2:>8.4f}")
text
Method                    | Train RMSE | Test RMSE |       R²
-----------------------------------------------------------------
Decision Tree             |     0.0000 |    0.7312 |   0.5944
Random Forest             |     0.1023 |    0.5031 |   0.7698
AdaBoost                  |     0.6234 |    0.6189 |   0.6723
Gradient Boosting         |     0.2891 |    0.4512 |   0.8124
XGBoost                   |     0.1456 |    0.4134 |   0.8301
RMSE Comparison: All Ensemble Methods Method RMSE 0.0 0.3 0.6 0.9 DTree RF AdaBoost GB XGBoost 0.731 0.503 0.619 0.451 0.413 Train Test

Decision Tree: maximal overfitting gap (Train=0, Test=0.731). AdaBoost underperforms GB and XGBoost — AdaBoost with stumps is primarily a bias reducer, and California Housing's relationship is complex enough to benefit more from deeper trees with regularization. XGBoost wins test RMSE (0.413, R²=0.830).

When It Works and When It Doesn't

Reach for XGBoost when you have structured data with 10k+ rows and need the best accuracy. The histogram split finding (tree_method='hist') makes it practical on large datasets, and the built-in L1/L2 regularization gives you hyperparameters to control overfitting without constraining tree depth. The early_stopping_rounds feature auto-tunes n_estimators, removing one of the harder tuning decisions in Gradient Boosting.

The limit: XGBoost is overkill for small datasets (< 1k samples) where a tuned Random Forest matches its accuracy with less tuning effort. It builds trees sequentially — no cross-tree parallelism — so training time scales linearly with n_estimators even with n_jobs=-1. The hyperparameter space is large enough that 30 random search iterations cover only a tiny fraction of it. And XGBoost's feature importance (Gain type) inherits the same high-cardinality bias as MDI in Random Forest.

ScenarioRecommendedReason
Quick baseline, interpretableRandom ForestNo tuning needed; OOB score for free
Clean data, high bias problemAdaBoostFast sequential correction of systematic errors
Medium dataset, best accuracyGradient BoostingWell-regularized, well-studied behavior
Large dataset (>100k rows), best accuracyXGBoost / LightGBMHistogram speed + built-in L1/L2 regularization
Real-time inference neededRandom ForestSmaller model, no sequential dependency
Noisy data / many outliersRandom ForestAveraging dilutes outlier impact; XGBoost sensitive

Trace Table: Final Ensemble Comparison on California Housing

PhaseFormulaValuesResult
Decision TreeTest RMSE0.7312R²=0.594 — overfit baseline
Random ForestTest RMSE0.5031R²=0.770 — bagged ensemble
AdaBoostTest RMSE0.6189R²=0.672 — stumps too weak for this task
Gradient BoostingTest RMSE0.4512R²=0.812 — residual fitting with depth=3
XGBoostTest RMSE0.4134R²=0.8301 — best overall
XGBoost improvementΔRMSE over baseline0.7312→0.4134−0.318 (43% reduction)
XGBoost improvementΔRMSE over GB0.4512→0.4134−0.038 (8% improvement)
XGBoost early stoppingBest iteration452/2000Auto-tuned n_estimators

This post requires the XGBoost math from the previous post — specifically what reg_alpha, reg_lambda, gamma, and min_child_weight control at the objective level. Early stopping connects to the staged prediction concept from Gradient Boosting: both expose the validation curve over rounds, but XGBoost automates the stopping. The final comparison table closes the loop on the entire ensemble series: Random Forest (posts 02–04), AdaBoost (posts 05–06), Gradient Boosting (post 07), and XGBoost (posts 08–09) are now benchmarked on identical data, making the accuracy/speed/interpretability tradeoffs concrete rather than theoretical. From here, LightGBM and CatBoost follow the same residual-boosting framework with different speed optimizations (leaf-wise growth, native categorical handling).

Test Your Understanding

  1. XGBoost RMSE=0.413, Gradient Boosting RMSE=0.451 — a 8% improvement. Both use max_depth=3–4, learning_rate=0.05–0.1, n_estimators=200–500. What specifically causes the gap? List at least 2 algorithmic differences between XGBoost and sklearn's GradientBoostingRegressor that could explain the improvement.

  2. Early stopping stopped at round 549, with best iteration 452 (50 rounds after best). If you set early_stopping_rounds=20 instead of 50, the model might stop earlier — say at round 472. Would this give a better or worse final model? When does smaller early_stopping_rounds help and when does it hurt?

  3. XGBClassifier achieved AUC=0.9972, while AdaBoost achieved AUC=0.9923 on Breast Cancer. Both are boosting methods. What difference in the two methods (beyond the obvious "XGBoost has more hyperparameters") most likely explains the AUC gap on this specific dataset?

  4. The feature importance comparison: Gain importance measures average gain per split; Weight counts total splits. A feature used 200 times at depth-10 (small gain per split) vs a feature used 3 times at depth-1 (large gain per split). Which gets higher Weight importance? Which gets higher Gain importance? For feature selection, which should you use?

  5. The final table shows AdaBoost Train RMSE=0.623 — higher than Random Forest (0.102). Both are ensemble methods. Why does AdaBoost have non-zero training error while Random Forest drives training error toward zero? What property of AdaBoost's stumps prevents it from memorizing training data even with n_estimators=200?

Chapter 66 of 81
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