Splitting Dataset in Machine Learning
Less than 500 views • Posted On Aug. 17, 2020
Data is split in three ways in Machine Learning -
- Training Data is used to train our model.
- This is the data that our model actually sees (both input and output) and learn from.
- Validation data is used to do a frequent evaluation of the model, fit on training data along with improving involved hyperparameters.
- This data plays its part when the model is actually training.
- Once our model is completely trained, testing data provides an unbiased evaluation.
- When we feed in the inputs of testing data, our model will predict some values without seeing the actual output.
- After prediction, we evaluate our model by comparing it with the actual output present in the testing data.
- This is how we evaluate and see how much our model has learned from the experiences feed in as training data, set at the time of training.
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