The Role of Validation Sets in Model Training | Train-Test-Validation Splits | Clearly explained!
๐ฅ In this video we referred to the validation set, a proportion from the overall dataset that has a very significant role! Validation dataset is used for final model selection and hyperparameter tuning, as well as to understand whether your model learns patterns or just overfits the training data. It gives a rough estimate of the performance of the model on an "unseen" data.
Remember to use test dataset for final evaluation. You can't use the results from the validation set only, as you used its feedback to tune your hyperparameters and select the best model!
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Chapters (11)
Introduction.
0:15
How different data splits are used in the model creation procedure?
0:41
How we define the validation set?
0:52
How is validation different from test and train?
1:00
What if you evaluate the model based on the validation set?
1:12
How is validation data used during the training?
1:33
At what point the validation performance will start declining?
1:48
How you select the best model based on the validation results?
1:54
How to evaluate the final performance?
1:59
The size of the validation set.
2:10
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