Link: Machine learning
What’s supervised learning
Supervised learning algorithms are trained with labels, meaning the correct output is given in the train data E.g. Negative or Positive review
How does the model work
The neural networks learns by comparing its actual outputs vs correct outputs, and they adjust the models to reduce the error rate.
Good for predicting future events using historical data
Method of split data
Split data to 3 sets to avoid using test data to validate the result. Note: on this course, it’s simplified by only use single train/test split
Used to train model parameters
To determine what model hyper-parameters to adjust
To get final performance metric. This also means we cannot go back to adjust model parameters after using model on the test set. Whatever the performance metric is, it’s the final measure.