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.
Companies are increasingly aware of the importance of properly analyzing and managing the huge amount of data they store on a daily basis.
Read More »The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corp.
Read More »The semantic web or "internet of knowledge" is an extension of the current web. Unlike the latter, the semantic web is based on proportional [...]
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 »