00no account needed
Titanic EDA and models
The starting point: data cleaning, feature engineering and eight classifiers compared by accuracy.
Example notebooks
Five runnable notebooks that take one dataset from a Kaggle starting point to monitored, versioned models, and an image example.
Five notebooks take the Titanic passenger list from a Kaggle starting point to monitored, versioned models, with their outputs committed. Each runs in about a minute; notebooks 01 to 04 need a free account.
01start here
Explain a model
Why one passenger died, which ticket would have saved him, two models that disagree, and what the forest learned overall.
uploadSHAPcounterfactualsfull analysis
02
Monitor in production
Two weeks of traffic with a shadow model, then a feed that sends fares in pence, caught by drift detection and named by an explanation.
hosted predictionsshadow modelsdrift
03
Improve and version
Error analysis with explanations, better features, then v2 shipped as a new version and compared with v1.
batch explanationsversionscompare insights
04
Bring your own model
XGBoost, explained with its own TreeSHAP values and through a scoring endpoint that Kaimei calls.
pre-computed attributionsremote inferenceprivate weights
images
Cats and dogs
A MobileNetV2 classifier uploaded with its photos attached, then asked why on new pictures. Each answer is drawn exactly as the dashboard draws it.
Grad-CAMocclusionLIME
kaimei.tabular.plot_explanation.