This article is about description for those who need to know what is the actual difference between the dataset split between the Training and Test sets in Machine Learning while training and classifying models.
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What is Training Data?
All the machine learning algorithms learn from data by finding relationships, developing understanding, making decisions, and building its confidence by using the training data we provide to a machine learning model. And this is to be noted that a machine learning model will perform based on what training data we have given to a model. The training data we will provide, the better the model will perform.
What is Test Data?
Once a machine learning model is trained by using a training set, then the model is evaluated on a test set. The test data provides a brilliant opportunity for us to evaluate the model. The test set is only used once our machine learning model is trained correctly using the training set. Generally, a test set is only taken from the same dataset from where the training set has been received.
Validation Set
Besides the Training and Test sets, there is another set which is known as a Validation Set. Validation Set is used to evaluate the model’s hyperparameters. Our machine learning model will go through this data, but it will never learn anything from the validation set. A Data Scientist use the results of a Validation set to update higher level hyperparameters.
How to Split the Dataset into Training and Test sets
Generally, a dataset should be split into Training and Test sets with the ratio of 80 per cent Training set and 20 per cent test set. This split of the Training and Test sets is ideal.
When to use A Validation Set with Training and Test sets
Now, as you know, sometimes the data needs to be split into three rather than only training and test sets. So the question arises when to use a Validation Set. I will just say that some models need substantial data to be trained with, in some cases models with very few hyperparameters will be easy to validate and prepare, in such instances you need to split the data into three sets. Still, the ratio of the validation set should be less if you have few hyperparameters.
If your model has many hyperparameters, then obviously you need to increase the proportion of validation set. In some cases, when your model will not have any hyperparameters, in such cases, you will not need a Validation Set.
What are Hyperparameters in Training and Test Sets?
A model has one or more model parameters that determine what it will predict given a new instance. A machine learning algorithm tries to find optimal values for these parameters such that the model generalises well to new cases. A hyperparameter is a parameter of the machine learning algorithm itself, not of the model.
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