Library "FunctionDecisionTree" Method to generate decision tree based on weights.
decision_tree(weights, depth) Method to generate decision tree based on weights. Parameters: weights: float array, weights for decision consideration. depth: int, depth of the tree. Returns: int array
Please give some example how the same can be implemented with indicators. Like i want to find out optimized setting for supertrend how can i do so using the below library. Any help in same direction will help me much.
Thanks for the lib and great work again.
Please give some example how the same can be implemented with indicators. Like i want to find out optimized setting for supertrend how can i do so using the below library. Any help in same direction will help me much.