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Feature importance: Churn rate and risk ratio — Machine Learning Zoomcamp Module 3

1. Churn rate: Difference between global mean of the target variable and mean of the target variable for categories of a feature. If this difference is greater than 0, it means that the category is less likely to churn, and if the...

Machine Learning Zoomcamp › Module 3: Machine Learning for Classification › Feature importance: Churn rate and risk ratio

Notes

1. Churn rate: Difference between global mean of the target variable and mean of the target variable for categories of a feature. If this difference is greater than 0, it means that the category is less likely to churn, and if the difference is lower than 0, the group is more likely to churn. The larger differences are indicators that a variable is more important than others.

2. Risk ratio: Ratio between mean of the target variable for categories of a feature and global mean of the target variable. If this ratio is greater than 1, the category is more likely to churn, and if the ratio is lower than 1, the category is less likely to churn. It expresses the feature importance in relative terms.

Functions and methods:

• df.groupby('x').y.agg([mean()]) - returns a dataframe with mean of y series grouped by x series
• display(x) displays an output in the cell of a jupyter notebook.

The entire code of this project is available in this jupyter notebook.

• Notes from Peter Ernicke

Key concepts

• Pandas
• Technical Indicators
• Jupyter Notebooks

Related notes

• mlz-m01-introduction-to-numpy
• mlz-m01-introduction-to-pandas
• mlz-m01-linear-algebra-refresher
• mlz-m01-setting-up-the-environment
• mlz-m01-summary
• mlz-m02-baseline-model-for-car-price-prediction-project
• mlz-m02-categorical-variables
• mlz-m02-computing-rmse-on-validation-data
• mlz-m02-data-preparation
• mlz-m02-exploratory-data-analysis
• mlz-m02-feature-engineering
• mlz-m02-linear-regression
• mlz-m02-linear-regression-vector-form
• mlz-m02-regularization
• mlz-m02-root-mean-squared-error-rmse
• mlz-m02-setting-up-the-validation-framework
• mlz-m02-training-linear-regression-normal-equation
• mlz-m02-tuning-the-model
• mlz-m02-using-the-model
• mlz-m03-churn-prediction-project
• mlz-m03-data-preparation
• mlz-m03-eda
• mlz-m03-explore-more
• mlz-m03-feature-importance-correlation
• mlz-m03-feature-importance-mutual-information
• mlz-m03-logistic-regression
• mlz-m03-model-interpretation
• mlz-m03-one-hot-encoding
• mlz-m03-setting-up-the-validation-framework
• mlz-m03-summary
• mlz-m03-training-logistic-regression-with-scikit-learn
• mlz-m03-using-the-model
• mlz-m04-accuracy-and-dummy-model
• mlz-m04-confusion-table
• mlz-m04-cross-validation
• mlz-m04-roc-auc
• mlz-m04-roc-curves
• mlz-m04-summary
• mlz-m06-data-cleaning-and-preparation
• mlz-m06-decision-trees-parameter-tuning
• mlz-m06-gradient-boosting-and-xgboost
• mlz-m06-xgboost-parameter-tuning
• mlz-m08-installation-of-tensorflow
• mlz-m10-creating-a-pre-processing-service

Sources

• Video
• Lesson file