Cross-validation is a technique used in machine learning to evaluate the performance of a statistical model, and to estimate the accuracy of the model on new data sets that have not been used to train the model.
Cross-validation is performed by splitting the dataset into a training set and a validation set. The model is trained with the training set and evaluated with the validation set. This process is repeated several times, with different divisions of the data into training and validation sets. At the end, the results of the different evaluations are averaged to obtain a more accurate measure of the model's performance.
Cross-validation is a useful technique to avoid overfitting in the model, as it allows to assess its generalisability. The technique can also be useful in model selection and optimisation of model parameters.
There are several types of cross-validation, including k-fold cross-validation, leave-one-out cross-validation, and stratified cross-validation. Each type has its own characteristics and may be more suitable for certain applications.
The commercial optimization software based on artificial intelligence must have feedback of the commercial actions carried out, of the nu [...]
Read More »Intelligent Process Automation in companies has changed in the world very rapidly in recent years. The COVID-19, the interr [...]
Read More »The fad coming from the USA that will force the incorporation of AI in the process Surely it is only recently that we have started to hear a new concept in [...]
Read More »There is a consensus among executives of the world's largest companies about the important impact that Artificial Intelligence (AI) will have on the [...]
Read More »