Contents
What is variable importance?
(My) definition: Variable importance refers to how much a given model “uses” that variable to make accurate predictions. The more a model relies on a variable to make predictions, the more important it is for the model. It can apply to many different models, each using different metrics.
Why is h20 important?
Water regulates our body temperature, acts as a lubricant for joints, aids in digestion and absorbing nutrients, and then it helps to flush waste from the body.
What are the 4 main functions of water in the body?
Of all the nutrients, water is the most critical as its absence proves lethal within a few days. Water’s importance in the human body can be loosely categorized into four basic functions: transportation vehicle, medium for chemical reactions, lubricant/shock absorber, and temperature regulator.
Is the variable importance of a GBM the same in H2O?
The following image shows variable importance for a GBM, but the calculation would be the same for Distributed Random Forest. While it is possible to get the raw variable importance for each feature, H2O displays each feature’s importance after it has been scaled between 0 and 1.
How to plot number of features in H2O?
The number of features shown in the plot (default is 10 or all if less than 10). h2o.std_coef_plot for GLM.
How is the importance of a variable determined?
Variable importance is determined by calculating the relative influence of each variable: whether that variable was selected to split on during the tree building process, and how much the squared error (over all trees) improved (decreased) as a result.
How are variable importances computed in distributed random forest?
Distributed Random Forest is a model that trains many decision trees in parallel and then averages their results. The variable importances are computed from the gains of their respective loss functions during tree construction. H2O uses squared error, and XGBoost uses a more complicated one based on gradient and hessian.