Course Wiki

Using the model — Machine Learning Zoomcamp Module 3

We trained the logistic regression model with the full training dataset (training + validation), considering numerical and categorical features. Thus, predictions were made on the test dataset, and we evaluated the model using the accuracy...

Machine Learning Zoomcamp › Module 3: Machine Learning for Classification › Using the model

Notes

We trained the logistic regression model with the full training dataset (training + validation), considering numerical and categorical features. Thus, predictions were made on the test dataset, and we evaluated the model using the accuracy metric.

In this case, the predictions of validation and test were similar, which means that the model is working well.

• Notes from Peter Ernicke

Key concepts

• Logistic Regression

Related notes

• mlz-m01-model-selection-process
• mlz-m03-churn-prediction-project
• mlz-m03-data-preparation
• mlz-m03-eda
• mlz-m03-explore-more
• mlz-m03-feature-importance-churn-rate-and-risk-ratio
• 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

Sources

• Video
• Lesson file