Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

Tree Settings

CART

Non-parametric decision tree regressor

Split Criterion

Max Depth

Unlimited — tree grows until pure leaves or min samples constraint.

Min Samples Split2

Minimum samples required to split an internal node.

Min Samples Leaf1

Minimum samples required at each leaf node.

Max Features

CCP Alpha (Pruning)Off

0 = no pruning1.0 = aggressive

Cost-complexity pruning: higher values remove branches that add little predictive power.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

Decision Trees are 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

Adj. R²

—

Awaiting training

Train R²

—

Awaiting training

Tree Depth

—

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

Tree Settings

CART

Non-parametric decision tree regressor

Split Criterion

Max Depth

Unlimited — tree grows until pure leaves or min samples constraint.

Min Samples Split2

Minimum samples required to split an internal node.

Min Samples Leaf1

Minimum samples required at each leaf node.

Max Features

CCP Alpha (Pruning)Off

0 = no pruning1.0 = aggressive

Cost-complexity pruning: higher values remove branches that add little predictive power.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

Decision Trees are 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

Adj. R²

—

Awaiting training

Train R²

—

Awaiting training

Tree Depth

—

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.

Decision Tree Regressor — Interactive Analysis