Model Explainer

Feature Importances

Model performance metrics

metric Score
accuracy 0.844
precision 0.73
recall 0.718
f1 0.724
roc_auc_score 0.807
pr_auc_score 0.615
log_loss 5.38

Confusion Matrix

How many false positives and false negatives?

Precision Plot

Does fraction positive increase with predicted probability?

Classification Plot

Distribution of labels above and below cutoff

ROC AUC Plot

Trade-off between False positives and false negatives

PR AUC Plot

Trade-off between Precision and Recall

Lift Curve

Performance how much better than random?

Cumulative Precision

Expected distribution for highest scores

Individual Predictions

Select Random Index

Selected index: None

Prediction

no index selected

Contributions Plot

How has each feature contributed to the prediction?
no index selected

Partial Dependence Plot

Contributions Table

How has each feature contributed to the prediction?
no index selected

What if...

Select Random Index

Selected index: None

Prediction

input data incorrect

Feature Input

Adjust the feature values to change the prediction
Selected: None

Contributions Plot

How has each feature contributed to the prediction?
input data incorrect

Partial Dependence Plot

input data incorrect

Contributions Table

How has each feature contributed to the prediction?
input data incorrect

Feature Dependence

Shap Summary

Ordering features by shap value

Shap Dependence

Relationship between feature value and SHAP value