Permutation importance is a technique used in machine learning to assess the relative importance of features in a prediction model. The idea is to measure the impact of the random removal or permutation of a feature on the performance of the model. In general, the greater the decrease in model performance after the removal or permutation of a feature, the greater its importance to the model.
Permutation importance is useful because it helps to identify the features that are most relevant to a particular prediction problem, which can guide feature selection and model optimisation. In addition, it can be used with different machine learning algorithms, including decision trees, linear models and neural networks.
Permutation importance can be computationally expensive, as it involves training and evaluating the model several times. However, efficient implementations of the technique are available in machine learning libraries such as Scikit-learn in Python, making it easy to use for data scientists and analysts.
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 »More and more companies are taking advantage of the relevant information they extract from the data they possess and generate to improve their processes and discover new ways to [...]
Read More »As a consequence of this pandemic and economic situation in which we have found ourselves for the last two years, with the intention of better protecting the [...]
Read More »The commercial optimization software based on artificial intelligence must have feedback of the commercial actions carried out, of the nu [...]
Read More »
