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.
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 rise of Artificial Intelligence (AI) in business is very topical. Its use is spreading and is changing, even, the models [...]
Read More »The current scenario we are experiencing in Spain with the COVID-19 health crisis has led to many companies having to carry out ER [...]
Read More »Today we are going to talk about how to foresee payment problems and foresee the problems in those customers who are currently not giving it to you. In G [...]
Read More »