随机砍伐森林的超参数调整

Question

I have used to below hyper parameters to train the model.

  rcf.set_hyperparameters(
        num_samples_per_tree=200,
        num_trees=250,
        feature_dim=1,
        eval_metrics =["accuracy", "precision_recall_fscore"])

is there any best way to choose the num_samples_per_tree and num_trees parameters.

what are the best numbers for both num_samples_per_tree and num_trees.

Answer 1

There are natural interpretations for these two hyper-parameters that can help you determine good starting approximations for HPO:

• num_samples_per_tree -- the reciprocal of this value approximates the density of anomalies in your data set/stream. For example, if you set this to 200 then the assumption is that approximately 0.5% of the data is anomalous. Try exploring your dataset to make an educated estimate.
• num_trees -- the more trees in your RCF model the less noise in scores. That is, if more trees are reporting that the input inference point is an anomaly then the point is much more likely to be an anomaly than if few trees suggest so.

The total number of points sampled from the input dataset is equal to num_samples_per_tree * num_trees . You should make sure that the input training set is at least this size.

(Disclosure - I helped create SageMaker Random Cut Forest)