FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Training data is the data you use to train an algorithm or machine learning model to predict the outcome you design your model to predict. If you are using supervised learning or some hybrid that includes that approach, your data will be enriched with data labeling or annotation.

Detailed explanation-2: -Training data is used in model training, or in other words, it’s the data used to fit the model. On the contrary, test data is used to evaluate the performance or accuracy of the model. It’s a sample of data used to make an unbiased evaluation of the final model fit on the training data.

Detailed explanation-3: -In machine learning, datasets are split into two subsets. The first subset is known as the training data-it’s a portion of our actual dataset that is fed into the machine learning model to discover and learn patterns. In this way, it trains our model. The other subset is known as the testing data.

Detailed explanation-4: -The main difference between training data and testing data is that training data is the subset of original data that is used to train the machine learning model, whereas testing data is used to check the accuracy of the model. The training dataset is generally larger in size compared to the testing dataset.

Detailed explanation-5: -A training set is a portion of a data set used to fit (train) a model for prediction or classification of values that are known in the training set, but unknown in other (future) data. The training set is used in conjunction with validation and/or test sets that are used to evaluate different models.