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Dealing with Underfitting (high training error) and overfitting (testing error \u003e\u003e training error). Validating models with cross validation methods.\u003cbr/\u003e\n\n## Analysing the 5 Cs of credit\n\n- Character - borrower's credit history / creditworthiness, customer segmentation, demographics, card type, usage\n- Capacity - income (history of stable income)\n- Capital - savings, invvestments\n- Collateral - loan, tenure\n- Conditions - purpose of credit, economy, employment type\n\u003cbr/\u003e\n\n## Error metrics \u003cBR/\u003e\n\nConfusion matrix:\n![](pics/recall.JPG)\n\nFor credit card data, **recall** is the most important, since we want to minimize false negatives (FN). \u003cBR/\u003e\nMeaning, the actual frauds that were not predicted correctly. \u003cbr/\u003e\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs1dewalker%2Fcredit-risk-modeling-in-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fs1dewalker%2Fcredit-risk-modeling-in-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs1dewalker%2Fcredit-risk-modeling-in-python/lists"}