Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

SVR Settings

ε-SVR

Epsilon-insensitive support vector regressor

Kernel

C (Regularization)10.00

0.01 = strong reg.10000 = weak reg.

Trades off margin width vs training error. Lower C = wider margin, more regularization.

Epsilon (ε-tube)0.20

0 = no tolerance1.0 = wide tube

Width of the epsilon-insensitive tube. Points inside are not penalized.

Gamma (kernel coefficient)

Defines influence range of each support vector. Used by RBF, Poly, and Sigmoid kernels.

Optimizer Settings▼

Shrinking heuristic & iteration limits

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

SVR is sensitive to feature scale — standard scaling is strongly recommended for best results.

Projection

2D visualization method

PCA for quick linear projection. UMAP for complex manifolds.

Parameter Guide

▼

Performance Metrics

R²

—

Awaiting training

RMSE

—

Awaiting training

MAE

—

Awaiting training

Adj. R²

—

Awaiting training

Train R²

—

Awaiting training

SV Density

—

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

SVR Settings

ε-SVR

Epsilon-insensitive support vector regressor

Kernel

C (Regularization)10.00

0.01 = strong reg.10000 = weak reg.

Trades off margin width vs training error. Lower C = wider margin, more regularization.

Epsilon (ε-tube)0.20

0 = no tolerance1.0 = wide tube

Width of the epsilon-insensitive tube. Points inside are not penalized.

Gamma (kernel coefficient)

Defines influence range of each support vector. Used by RBF, Poly, and Sigmoid kernels.

Optimizer Settings▼

Shrinking heuristic & iteration limits

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

SVR is sensitive to feature scale — standard scaling is strongly recommended for best results.

Projection

2D visualization method

PCA for quick linear projection. UMAP for complex manifolds.

Parameter Guide

▼

Performance Metrics

R²

—

Awaiting training

RMSE

—

Awaiting training

MAE

—

Awaiting training

Adj. R²

—

Awaiting training

Train R²

—

Awaiting training

SV Density

—

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.

Support Vector Regression (SVR) — Interactive Analysis