01_Training Loan Request
This workflow trains a classification model using a Random Forest algorithm to approve or disapprove loan requests. The dataset we used is the German Credit Data Set, provided by University of California Archive for Machine Learning and Intelligent Systems. This dataset contains loan requests and information about the applicants' age, marital status, checking account balance and employment. These pieces of information will be the input features to the model. If the loan request was approved, the target variable is 1 ( = credit-worthy applicant), if rejected 2 ( = risky applicant). The workflow implements the following steps:
Read more on the topic Credit Scoring on the KNIME Blog: https://www.knime.com/blog/how-to-do-credit-scoring
URL: How to Optimize the Classification Threshold https://www.knime.com/blog/from-modeling-to-scoring
URL: Scoring Metrics eBook https://www.knime.com/knimepress/scoring-metrics-evaluating-machine-learning-models
URL: KNIME Blog: Credit Scoring https://www.knime.com/blog/how-to-do-credit-scoring
URL: German Credit Dataset https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data
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