Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

Network Architecture

MLP

Multi-Layer Perceptron configuration

Architecture Preset

Hidden Layers

Comma-separated neuron counts. E.g. “128,64” = two hidden layers with 128 and 64 neurons.

Activation Function

Rectified Linear Unit — fast training, works well for most problems.

Optimizer

Adam is robust and works well by default. Recommended for most tasks.

L2 Penalty (α)0.0001

L2 weight decay prevents overfitting by penalising large weights.

Training Settings

▼

Learning rate, iterations, early stopping

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

Neural networks require scaled inputs — standard scaling is strongly recommended for stable convergence.

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

Architecture

—

Awaiting training

Iterations

—

Awaiting training

Parameters

—

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.

Loading…

Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

Network Architecture

MLP

Multi-Layer Perceptron configuration

Architecture Preset

Hidden Layers

Comma-separated neuron counts. E.g. “128,64” = two hidden layers with 128 and 64 neurons.

Activation Function

Rectified Linear Unit — fast training, works well for most problems.

Optimizer

Adam is robust and works well by default. Recommended for most tasks.

L2 Penalty (α)0.0001

L2 weight decay prevents overfitting by penalising large weights.

Training Settings

▼

Learning rate, iterations, early stopping

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

Neural networks require scaled inputs — standard scaling is strongly recommended for stable convergence.

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

Architecture

—

Awaiting training

Iterations

—

Awaiting training

Parameters

—

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.

MLP Neural Network Regressor — Interactive Analysis