[Tech With Tim] TensorFlow 2.0 Tutorial - Training the Model - Text Classification P3

*** Python Neural Networks with TensorFlow 7
: Text Classification P3

- Validation Data
- Training the Model
- Testing the Model


*** Text-Based Tutorial: https://techwithtim.net/tutorials/pyt...


*** About Train, Validation and Test Sets in Machine Learning


● Training Dataset

- The sample of data used to fit the model.
- The actual dataset that we use to train the model (weights and biases in the case of Neural Network). The model sees and learns from this data


● Validation Dataset

- The sample of data used to provide an unbiased evaluation of a model fie 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.

- The validation set is used to evaluate a given model, but this is for frequent evaluation. We as machine learning engineers use this data to fine-tune the model hyperparameters. Hence the model occasionally sees this data, but never does it "Learn" from this. We use the validation set results and update higher level hyperparameters. So the validation set in a way affects a model, but indirectly.


● Test Dataset

- The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.

- The Test dataset provides the gold standard used t evaluate the model. It is only used once a model is completely trained(using the train and validation sets). The test set is generally what is used to evaluate competing models (For example on many Kaggle competitions, the validation set is released initially along with the training set and the actual test set is only released when the competition is about to close, and it is the result of the the model on the Test set that decides the winner). Many a times the validation set is used as the test set, but it is not good practice. The test set is generally well curated. It contains carefully sampled data that spans the various classes that the model would face, when used in the real world.