Overview#Training dataset that is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model.
The model (e.g. a neural net or a naive Bayes classifier) is trained on the Training dataset using a supervised Learning method (e.g. gradient descent or stochastic gradient descent). In practice, the Training dataset often consist of pairs of an input vector and the corresponding answer vector or scalar, which is commonly denoted as the target. The current model is run with the Training dataset and produces a result, which is then compared with the target, for each input vector in the Training dataset. Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted. The model fitting can include both variable selection and parameter estimation.
More Information#There might be more information for this subject on one of the following:
- Cost function
- Hidden layer
- Hidden node
- Loss function
- Machine Learning
- Supervised Learning