Machine Learning Algorithms

Modeling/Grid search/Cross-validation

Modeling implies the choice of the classification/clustering algorithm that best suits every specific task. We have discussed different methods and the reader should be able to understand when a set of algorithms is a reasonable candidate, and when it's better to look for another strategy. However, the success of a machine learning technique often depends on the right choice of each parameter involved in the model as well. As already discussed, when talking about data augmentation, it's very difficult to find a precise method to determine the optimal values to assign, and the best approach is always based on a grid search. scikit-learn provides a very flexible mechanism to investigate the performance ...