Training in machine learning refers to the process of feeding a machine learning algorithm with data to help identify and learn good values for all attributes involved. The goal of training a model is to find a set of weights and biases that have low loss, on average, across all examples. The training data must contain the correct answer, which is known as a target or target attribute. The learning algorithm finds patterns in the training data that map the input data attributes to the target and it outputs an ML model that captures these patterns. The accuracy of the training dataset or the validation dataset is critical for the precision of the model.
There are two main types of machine learning models: supervised and unsupervised learning. In supervised learning, the training data contains both the input and output values. Each set of data that has the inputs and the expected output is called a supervisory signal. The training is done based on the deviation of the processed result from the documented result when the inputs are fed into the model. In unsupervised learning, the model creates a mathematical representation among the data features themselves.
The process of training a model involves providing an ML algorithm with training data to learn from. The algorithm is fed with sufficient training data to learn from, which consists of the sample output data and the corresponding sets of input data that have an influence on the output. The training model is used to run the input data through the algorithm to correlate the processed output against the sample output. The result from this correlation is used to modify the model. This iterative process is called “model fitting” .
In summary, training in machine learning is a crucial step in the development of a working model that can then be validated, tested, and deployed. It involves feeding an ML algorithm with data to help identify and learn good values for all attributes involved. The accuracy of the training dataset or the validation dataset is critical for the precision of the model.
