Yes estimators in the class is bound to the random estate split function trainee steeple. Also inside the class there is a function which counts the number of times. Which features were encountered in the selecte conditions. Writes the result to self. feat recount. Then you can see the frequency of use of each feature. The findepath and get rule functions were take from the user vlemaistre thanks to him on the Stack Overflow website . And this is documentation on the structure of the tree. Lets test For the test lets take several datasets from Kaggle and compare.
On the left are the original
Random Branch with basic Random Forest solutions. I didnt delve into the specifics of the data. The first task is to preict customer churn registration is require to download data. Actually the code for the transformations and parameters for Random. Forest were take from this basic solution Code Precision and recall curve for Random. Forest Precision and recall curve Taiwan WhatsApp Number List for Random Forest Now lets try the Random Branch option. But first lets duplicate all the features with the sign. So that in cases of negative correlation with the class we nee this feature can also fall into the right branch.
Branch Precision and recall curves
To do this use the adderev function Code Next we will train the Random Branch with default parameters changing only neestimators to . And if you use includeeleft True then neestimators can be set to . Code Here is an example of a tree where the right branch satisfies all the Costa Rica Phone Number List conditions of the maxeright option with the leafethreshold . parameter. The share is greater than and the number of objects of the positive class is the maximum among all leaves with the same share of the positive class. Decision tree example Decision tree example Now lets preict sequences of features without duplicates option b Code.