Machine learning models are built with the help of data sets used at various stages of development. There are different types of data sets used on machine learning of AI-based model development like training data, validation data, and test data sets.
The questions are why data is split and what are these data types. The data is split into different types of training, validation, and test data, and here we will discuss what are these types of data and where or how they used in various stages of machine learning development.
Why Data is Split into various types?
Developing a machine learning model is the process that extrapolates to input samples that it has never noticed before. This kind of work needs to expose the ML model to a certain number of data inputs to make the output accuracy at the best level. And these data inputs are split into multiple steps and each model has to go through before it is used in real life.
#1 Data Examination by ML Model
#2 Model Learning from Mistakes
#3 Output Quality and Accuracy Check
As you can see each step is different resulting in each data is treated differently at different stages of model development. Hence, we need to decide here which data in the data set is playing an important role in which stage of ML development.
Training Data Sets
This is the first stage of datasets that comprises a set of input examples that the model will be fit into or used to train the model while adjusting the various parameters like weights, height, and other factors in the context of neural networks. Simply, you can say training data sets are used to train the model with data used in real-life that gathered as machine learning training data.
Also Read: How to Validate Machine Learning Models: ML Model Validation Methods?
Validation Data Sets
The second stage is evaluating the model predictions and learn from mistakes before validating the data sets. Through the evolution process, estimating the mistakes or the losses the model yields on the validation set at any given point of time. It helps to know the machine learning engineers how accurate is the model output which is very much important. It helps to tune its parameters depending on the frequent evaluation results on the validation set.
Also Read: How to Measure Quality While Training the Machine Learning Models?
Testing Data Sets
This data sets type is you can say the final evaluation that a model needs to go through after the training stage in model development. This step is critical to test the final testing of the model that helps to generalizability and find out the working accuracy of the model. However, every AI or machine learning engineer need to be subjective and unbiased by not exposing such models to the test set until the training phase is fully completed. This kind of positive approach in ML model training development is considered as the final accuracy measure to be reliable.
The machine learning model training involves looking at training examples and learning from how much the model is inaccurate by evaluating through the ML model validation data sets. However, the most important or valuable indicator of the accuracy of a model is a result of testing the model on the testing set when the model training is fully completed to make sure it can work with the best accuracy without showing any inaccuracy.