Automated Machine Learning
ML Explainability

Use AutoML to understand your data.
Explain your ML models.

ML Explainability Overview

Model Description

Keep all your model information in one place. Save the hyperparameters setup, validation strategy, optimized metric and learning time.

Model Hyperparameters
Metric Details

Metric Details

Track many metrics at once. Use automated threshold search for binary classification. Check confussion matix for classification problems.

Learning Curve

Always check learning curves from your model. Avoid overfitting with early-stopping (set on by default).

Learning Curve
Decision Tree Visualization

Decision Tree Visualization

Visualize your Decision Trees to better understand data and ML model.

Linear Model Coefficients

Inspect your Linear Model Coefficients.

Linear Model Coefficients
Permutation Based Feature Importance

Permutation-Based Feature Importance

Compute Feature Importance for any Machine Learning model with Permutation-Based method.

SHAP-Based Feature Importance

Compute Feature Importance with SHAP values.

SHAP Based Feature Importance
SHAP Dependence Plots

SHAP Dependency Plots

Discover data dependencies with SHAP values.

SHAP Decision Plots

Check which features are used when ML model makes decision.

SHAP Decision Plots

Check more mljar features

Golden Features

K-Means Features

Model Ensembling

Automatic Documentation