Model evaluation is a critical process in the development of machine learning models and involves measuring and comparing the performance of models to determine their accuracy and effectiveness. The goal of model evaluation is to determine whether a model is capable of making accurate and consistent predictions on new data.
In the model evaluation process, a test data set is used to test the model and measure its performance in terms of specific metrics, such as accuracy, sensitivity, specificity, F1-score, and others. These metrics allow us to determine how well the model performs on the task for which it has been trained.
In addition to performance metrics, cross-validation techniques can also be used to assess the model's ability to generalise to new data. This is done by dividing the dataset into several training and test subsets and evaluating the model on each subset to determine its ability to make accurate predictions on unseen data.
There is a consensus among executives of the world's leading companies about the crucial impact that Artificial Intelligence (AI) will have on the [...]
Read More »Artificial intelligence is increasingly present in companies and its growth is being applied in practically all sectors. When the end [...]
Read More »ERP stands for Enterprise Resource Planning and is a computerized planning and business management system capable of integrating the information [...]
Read More »Since 2008, several countries have enacted legislation that recognizes the importance of integrating artificial intelligence (AI) into key areas of life [...]
Read More »
