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K-S Model Evaluation​

Kolmogorov-Smirnov

K-S or Kolmogorov-Smirnov chart measures performance of classification models. More accurately, K-S is a measure of the degree of separation between the positive and negative distributions.

https://www.saedsayad.com/model_evaluation_c.htm

Out Of Time Validation​

https://towardsdatascience.com/why-isnt-out-of-time-validation-more-ubiquitous-7397098c4ab6

Evaluating Supervised Learning​

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K-fold Cross Validation​

One way to further protect against overfitting is K-fold cross validation

  • Split your data into K randomly-assigned segments
  • Reserve one segment as your test data
  • Train on the remaining segments and measure performance against the test set
  • Repeat, using each segment as the test data and the remaining data for training
  • Take the average of the resulting accuracy scores

Using cross-validation will run the validation on multiple folds of the data, which reduces the overfitting.

https://machinelearningmastery.com/k-fold-cross-validation

TSA​

Model should be,

  • Transparent,
  • Secure, and
  • Auditable