Dataset

Upload CSV or Excel with a target column

Try with a sample dataset

Pick one to explore instantly — no upload needed

Kernel & Regularization

SVM

Core SVM configuration

Kernel

Radial Basis Function. Good for most non-linear problems.

C (Regularization)1.00

Small margin (0.001)Large margin (1000)

Gamma

Controls how far the influence of a single training example reaches.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Class Balancing

Shrinking Heuristic

Preprocessing

Feature transformations

Enable preprocessing

Interaction Terms

Add pairwise feature products (A × B) to capture non-linear relationships

Advanced settings

Projection

2D visualization method

PCA for quick linear projection. UMAP for complex manifolds.

Parameter Guide

Click to expand

Performance Metrics

Accuracy

—

Awaiting training

Precision

—

Awaiting training

Recall

—

Awaiting training

F1 Score

—

Awaiting training

ROC AUC

—

Awaiting training

Dataset

—

Awaiting training

Classification Visualization

Classification Results

Interactive scatter plot of classification results

Run training to render the scatter plot

Advanced Analysis

Deep Dive Analysis

Run training or prediction to populate analysis

Run training or prediction to see results here.

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 target column

Try with a sample dataset

Pick one to explore instantly — no upload needed

Kernel & Regularization

SVM

Core SVM configuration

Kernel

Radial Basis Function. Good for most non-linear problems.

C (Regularization)1.00

Small margin (0.001)Large margin (1000)

Gamma

Controls how far the influence of a single training example reaches.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Class Balancing

Shrinking Heuristic

Preprocessing

Feature transformations

Enable preprocessing

Interaction Terms

Add pairwise feature products (A × B) to capture non-linear relationships

Advanced settings

Projection

2D visualization method

PCA for quick linear projection. UMAP for complex manifolds.

Parameter Guide

Click to expand

Performance Metrics

Accuracy

—

Awaiting training

Precision

—

Awaiting training

Recall

—

Awaiting training

F1 Score

—

Awaiting training

ROC AUC

—

Awaiting training

Dataset

—

Awaiting training

Classification Visualization

Classification Results

Interactive scatter plot of classification results

Run training to render the scatter plot

Advanced Analysis

Deep Dive Analysis

Run training or prediction to populate analysis

Run training or prediction to see results here.

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 Machine (SVM) Classifier — Interactive Analysis