The test set consists of a set of labelled examples similar to the training set, but which the model has not seen before during its training process. The machine learning model uses the test set to evaluate its ability to accurately generalise and predict output labels for new examples.
The test set consists of a set of labeled examples similar to the training set, but which the model has not seen before during its training process. The machine learning model uses the test set to evaluate its ability to accurately generalize and predict output labels for new examples.
The evaluation of the model on the test set helps determine whether the model is overfitting or underfitting the training data. Overfitting occurs when the model over-fits the training data and does not generalise well to new data, while under-fitting occurs when the model does not fit the training data well enough and cannot accurately predict the test data.
It is important to have an independent test set to evaluate the model's performance, as using the training set for evaluation can lead to an optimistic assessment of the model's accuracy.
A few days ago we were able to attend a pioneering event in the world of Retail, the Retail Future 2022 fair. In its fifth edition, and under the slogan "Challenge [...]
Read More »Data Mining is a process of exploration and analysis of large amounts of data, with the objective of discovering patterns, relationships and trends that can be [...]
Read More »In this article we are going to focus on how artificial intelligence (AI) can increase efficiency and reduce costs for your company by [...]
Read More »The Big Data market is booming. Although the need to transform data into information for decision making is not new, the need to [...]
Read More »
