Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

Forest Settings

Ensemble

Bagged ensemble of decision trees

Number of Trees100

10 = fast500 = accurate

Split Criterion

Max Depth

Unlimited — trees grow 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

Random feature subset per split. √n is the standard for regression forests.

Bootstrap Sampling

Each tree trained on a bootstrap sample. Enables OOB score.

CCP Alpha (Pruning)Off

0 = no pruning1.0 = aggressive

Cost-complexity pruning applied to each tree in the ensemble.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

Random Forests 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

Train R²

—

Awaiting training

OOB R²

—

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

Forest Settings

Ensemble

Bagged ensemble of decision trees

Number of Trees100

10 = fast500 = accurate

Split Criterion

Max Depth

Unlimited — trees grow 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

Random feature subset per split. √n is the standard for regression forests.

Bootstrap Sampling

Each tree trained on a bootstrap sample. Enables OOB score.

CCP Alpha (Pruning)Off

0 = no pruning1.0 = aggressive

Cost-complexity pruning applied to each tree in the ensemble.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

Random Forests 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

Train R²

—

Awaiting training

OOB R²

—

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.

Random Forest Regressor — Interactive Analysis