F1-score

In theory a good model (one that makes the right predictions) will be one that has both high precision, as well as high recall. In practice however, a model has to make compromises between both metrics. Thus, it can be hard to compare the performance...

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False Negatives (FN)

Assume a data set that includes examples of a class Non-default and examples of class  Default. Assume further that you’re evaluating your model’s performance to predict examples of class Non-default.False negatives is a field in the confusion...

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False Positive (FP)

Assume a data set that includes examples of a class Non-default and examples of class  Default. Assume further that you’re evaluating your model’s performance to predict examples of class Non-default.False positives is a field in the confusion...

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Feature

A feature is an input variable. It can be numeric or categorical. For example, a house can have the following features: number of rooms (numeric), neighbourhood (categorical), street name (categorical).  The term “variable” throughout the Evispot...

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Financing cost

Financing cost or the cost of funds is a reference to the interest rate paid by financial institutions for the funds that they use in their business. The cost of funds is one of the most important input costs for a financial institution since a lower...

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Machine learning in credit decisions

Leveraging machine learning for smarter lending and obtain insights into the technology behind 100% transparent machine learning models.

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