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Credit risk scoring project — Machine Learning Zoomcamp Module 6

In this session we'll learn about decision trees and ensemble learning algorithms. The questions that we try to address this week are, 'What are decision trees? How are they different from ensemble algorithms? How can we implement and...

Machine Learning Zoomcamp › Module 6: Decision Trees and Ensemble Learning › Credit risk scoring project

Notes

In this session we’ll learn about decision trees and ensemble learning algorithms. The questions that we try to address this week are, “What are decision trees? How are they different from ensemble algorithms? How can we implement and fine-tune these models to make binary classification predictions?”

To be specific, we’ll use credit scoring data to build a model that predicts whether a bank should lend loan to a client or not. The bank takes these decisions based on the historical record.

In the credit scoring classification problem,

• if the model returns 0, this means, the client is very likely to payback the loan and the bank will approve the loan.
• if the model returns 1, then the client is considered as a defaulter and the bank may not approve the loan.

Add notes from the video (PRs are welcome)

• Notes from Peter Ernicke

Key concepts

• Decision Trees

Related notes

• mlz-m01-model-selection-process
• mlz-m05-summary
• mlz-m06-data-cleaning-and-preparation
• mlz-m06-decision-tree-learning-algorithm
• mlz-m06-decision-trees
• mlz-m06-decision-trees-parameter-tuning
• mlz-m06-ensemble-learning-and-random-forest
• mlz-m06-explore-more
• mlz-m06-gradient-boosting-and-xgboost
• mlz-m06-selecting-the-best-model
• mlz-m06-summary
• mlz-m06-xgboost-parameter-tuning

Sources

• Video
• Lesson file