Dataset

Upload CSV or Excel with a target column

Try with a sample dataset

Pick one to explore instantly — no upload needed

Model Type

Naive Bayes

Bayesian classifier variant

Variant

Assumes features follow a normal distribution. Best for continuous numeric data.

Var Smoothing10^-9

More precise (10⁻¹²)More smooth (10⁻¹)

Added to feature variances to prevent division by zero. Larger values regularize more aggressively.

Learn Class Priors

When on, learns class probabilities from data. When off, uses uniform priors.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Probability Calibration

Isotonic calibration — Naive Bayes probabilities are often poorly calibrated

ℹ️Naive Bayes tends to produce extreme probabilities (near 0 or 1) that are rarely well-calibrated. Enable calibration for reliable confidence scores, especially if you use predicted probabilities downstream.

Preprocessing

Feature transformations

Enable preprocessing

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

Model Type

Naive Bayes

Bayesian classifier variant

Variant

Assumes features follow a normal distribution. Best for continuous numeric data.

Var Smoothing10^-9

More precise (10⁻¹²)More smooth (10⁻¹)

Added to feature variances to prevent division by zero. Larger values regularize more aggressively.

Learn Class Priors

When on, learns class probabilities from data. When off, uses uniform priors.

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Probability Calibration

Isotonic calibration — Naive Bayes probabilities are often poorly calibrated

Preprocessing

Feature transformations

Enable preprocessing

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.

Naive Bayes Classifier — Interactive Analysis