Extra trees, just like Random Forests, are an ensemble model. In addition to sampling features at each stage of splitting the tree, it also samples random threshold at which to make the splits. The additional randomness may improve the ability of the model to generalize (compared to a random forest) and may yield better results.
Adjusts the number of features to sample at each split.
Adjusts the number of features to sample at each split.
Number of cores used for parallel training. Using more cores leads to faster training but at the expense of more memory consumption, especially for large training datasets.