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Battling Fraud Through Innovation: Your Data Quality Depends on it

We talk a lot in the market research industry about what needs to change and the challenges we face, but there are some notable innovations happening as well. In fact, we’re seeing positives like rising respondent engagement with suppliers focusing on experiences and using technology like automation to even turn down “bad” studies that are long and complicated.

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Optimizing portfolio value with Amazon SageMaker automatic model tuning

Financial institutions that extend credit face the dual tasks of evaluating the credit risk associated with each loan application and determining a threshold that defines the level of risk they are willing to take on. The evaluation of credit risk is a common application of machine learning (ML) classification models.

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Optimizing portfolio value with Amazon SageMaker automatic model tuning | Amazon Web Services

Financial institutions that extend credit face the dual tasks of evaluating the credit risk associated with each loan application and determining a threshold that defines the level of risk they are willing to take on. The evaluation of credit risk is a common application of machine learning (ML) classification models.

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7 Simple Tricks to Handle Complex Machine Learning Issues

We propose simple solutions to important problems that all data scientists face almost every day. In short, a toolbox for the handyman, useful to busy professionals in any field.
1. Eliminating sample size effects. Many statistics, such as correlations or R-squared, depend on the sample size, making it difficult to compare values computed on two data sets of different sizes.

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