Model Explainer

Feature Importances

Model performance metrics

metric Score
accuracy 0.45
precision 0.982
recall 0.436
f1 0.604
roc_auc_score 0.685
pr_auc_score 0.981
log_loss 0.158

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: 7244898

Prediction

label probability logodds
0 9.6 % -2.245
1 90.4 % 2.245

Feature Input

Adjust the feature values to change the prediction
Selected: 7244898

Contributions Plot

How has each feature contributed to the prediction?

Partial Dependence Plot

Contributions Table

How has each feature contributed to the prediction?
Reason Effect
Average of population 3.37
vote_count = 773.0 -0.11
vote_average = 6.900000095367432 +0.04
occupation_sales/marketing = 1.0 -0.47
title_vectorized = 0.26716333627700806 +0.06
age = 32.0 -0.01
occupation_K-12 student = 0.0 +0.03
occupation_retired = 0.0 -0.03
genre_names = 0.9429297704024708 -0.09
occupation_artist = 0.0 +0.03
occupation_self-employed = 1.0 -0.59
occupation_college/grad student = 0.0 +0.02
gender = 1.0 -0.02
overview_vectorized = -0.004784238990396261 +0.0
occupation_other or not specified = 0.0 -0.01
occupation_academic/educator = 0.0 +0.01
occupation_writer = 0.0 +0.01
production_countries = 0.9825783972124886 +0.02
occupation_executive/managerial = 0.0 -0.01
occupation_programmer = 0.0 -0.0
occupation_tradesman/craftsman = 0.0 +0.0
occupation_homemaker = 0.0 +-0.0
occupation_unemployed = 0.0 +0.0
original_language = 0.9593411175979983 +0.0
occupation_technician/engineer = 0.0 -0.0
occupation_doctor/health care = 0.0 +-0.0
occupation_scientist = 0.0 -0.0
occupation_lawyer = 0.0 +0.0
occupation_customer service = 0.0 +0.0
occupation_clerical/admin = 0.0 +0.0
movie_id_vectorized = -0.0021286881528794765 -0.0
occupation_farmer = 0.0 +0.0
Other features combined +0.0
Final prediction 2.25

Feature Dependence

Shap Summary

Ordering features by shap value

Shap Dependence

Relationship between feature value and SHAP value