Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

Boosting Settings

XGBoost

Optimised gradient boosting with regularization

Boosting Rounds100

10 = fast1000 = accurate

Learning Rate (eta)

0.30

0.01 = conservative1.0 = aggressive

XGBoost default: 0.3. Lower values need more rounds. Auto suggests 0.3 for 100 rounds.

Max Depth6

1 = stumps20 = deep

Objective

Subsample (rows)1.00

Col. Sample / Tree1.00

Fraction of features sampled per tree. < 1.0 adds randomness & reduces overfitting.

Regularization

XGBoost built-in L1/L2 + structural penalties

Gamma (min split loss)0.0

0 = no penalty10 = aggressive pruning

L1 Reg. (alpha)0.0

L1 on leaf weights — promotes sparsity. Default: 0.

L2 Reg. (lambda)1.0

L2 on leaf weights — smooths predictions. Default: 1.0.

Advanced

Early Stopping

Halt training when validation stalls

Enable

All 100 rounds will be trained. Enable to auto-stop at optimal round.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

XGBoost is scale-invariant — scaling is optional. Imputation is still recommended for missing values.

Projection

2D visualization method

PCA for quick linear projection. UMAP for complex manifolds.

Parameter Guide

Click to expand

Performance Metrics

R²

—

Awaiting training

RMSE

—

Awaiting training

MAE

—

Awaiting training

Train R²

—

Awaiting training

Rounds

—

Awaiting training

Learn Eff.

—

Awaiting training

Samples

—

Awaiting training

Actual vs Predicted

Train the model to see actual vs predicted scatter

Advanced Analysis

Deep Dive Analysis

Run training to populate analysis

Run training to populate this tab

Job History

Recent jobs — auto-pruned to keep 10 per type

No jobs recorded yet. Run a clustering, classification, or pipeline job to see results here.

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Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

Boosting Settings

XGBoost

Optimised gradient boosting with regularization

Boosting Rounds100

10 = fast1000 = accurate

Learning Rate (eta)

0.30

0.01 = conservative1.0 = aggressive

XGBoost default: 0.3. Lower values need more rounds. Auto suggests 0.3 for 100 rounds.

Max Depth6

1 = stumps20 = deep

Objective

Subsample (rows)1.00

Col. Sample / Tree1.00

Fraction of features sampled per tree. < 1.0 adds randomness & reduces overfitting.

Regularization

XGBoost built-in L1/L2 + structural penalties

Gamma (min split loss)0.0

0 = no penalty10 = aggressive pruning

L1 Reg. (alpha)0.0

L1 on leaf weights — promotes sparsity. Default: 0.

L2 Reg. (lambda)1.0

L2 on leaf weights — smooths predictions. Default: 1.0.

Advanced

Early Stopping

Halt training when validation stalls

Enable

All 100 rounds will be trained. Enable to auto-stop at optimal round.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

XGBoost is scale-invariant — scaling is optional. Imputation is still recommended for missing values.

Projection

2D visualization method

PCA for quick linear projection. UMAP for complex manifolds.

Parameter Guide

Click to expand

Performance Metrics

R²

—

Awaiting training

RMSE

—

Awaiting training

MAE

—

Awaiting training

Train R²

—

Awaiting training

Rounds

—

Awaiting training

Learn Eff.

—

Awaiting training

Samples

—

Awaiting training

Actual vs Predicted

Train the model to see actual vs predicted scatter

Advanced Analysis

Deep Dive Analysis

Run training to populate analysis

Run training to populate this tab

Job History

Recent jobs — auto-pruned to keep 10 per type

No jobs recorded yet. Run a clustering, classification, or pipeline job to see results here.

XGBoost Regressor — Interactive Analysis