Difference between revisions of "Train, Validate, and Test"

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* [[Evaluation Measures - Classification Performance]]
 
* [[Evaluation Measures - Classification Performance]]
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* [http://medium.com/datadriveninvestor/data-science-essentials-why-train-validation-test-data-b7f7d472dc1f Data Science essentials: Why train-validation-test data? | Sagar Patel - Medium]
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* [http://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7 About Train, Validation and Test Sets in Machine Learning | Tarang Shah - Towards Data Science]
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* [http://machinelearningmastery.com/difference-test-validation-datasets/?source=post_page--------------------------- What is the Difference Between Test and Validation Datasets? | Jason Brownlee - Machine Learning Mastery]
  
Confusion Matrix, Precision, Recall, F Score, ROC Curves, trade off between True Positive Rate and False Positive Rate.
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* Training Dataset: The sample of data used to fit the model.
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* Validation Dataset: The sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration.
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* Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
  
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Revision as of 17:14, 25 July 2019

YouTube search... ...Google search

  • Training Dataset: The sample of data used to fit the model.
  • Validation Dataset: The sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration.
  • Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.

1*OJVhBtg5YgeW7rKXoxKQxg.png