decision-tree

问题 I'm implementing AdaBoost(Boosting) that will use CART and C4.5. I read about AdaBoost, but i can't find good explenation how to join AdaBoost with Decision Trees. Let say i have data set D that have n examples. I split D to TR training examples and TE testing examples. Let say TR.count = m, so i set weights that should be 1/m, then i use TR to build tree, i test it with TR to get wrong examples, and test with TE to calculate error. Then i change weights, and now how i will get next Training

问题 I'm implementing AdaBoost(Boosting) that will use CART and C4.5. I read about AdaBoost, but i can't find good explenation how to join AdaBoost with Decision Trees. Let say i have data set D that have n examples. I split D to TR training examples and TE testing examples. Let say TR.count = m, so i set weights that should be 1/m, then i use TR to build tree, i test it with TR to get wrong examples, and test with TE to calculate error. Then i change weights, and now how i will get next Training

问题 I want to get correct prediction result. Prediction is based on 5 values. When I had a 5 inputs with one values in every input, prediction was correct, but after changing structure to 1 input with 5 value in it, prediction became incorrect. I think it is due to select2 library. Maybe the other 4 values is not recognized, that`s why prediction is incorrect, but I don't fully understand. Code: app.py: import os import csv import prediction from flask import Flask, jsonify, make_response, render

问题 If the input to any node of tree is the shown data, what will be the best split? Any split will have lesser children's accuracy than parent's accuracy, right? So even accuracy is decreasing will we go on splitting? 回答1: Without getting the specific data, this is hard to answer But simulating a similiar data can give the rough idea. Here's a tree for such data with max_depth of 3 The first split takes all the white dots on the right, and classifies them. The second split takes all the white

问题 If the input to any node of tree is the shown data, what will be the best split? Any split will have lesser children's accuracy than parent's accuracy, right? So even accuracy is decreasing will we go on splitting? 回答1: Without getting the specific data, this is hard to answer But simulating a similiar data can give the rough idea. Here's a tree for such data with max_depth of 3 The first split takes all the white dots on the right, and classifies them. The second split takes all the white

问题 i was trying to plot the decision tree which is formed with GridSearchCV, but its giving me an Attribute error. AttributeError: 'GridSearchCV' object has no attribute 'n_features_' However if i try to plot a normal decision tree without GridSearchCv, then it successfully prints. code [decision tree without gridsearchcv] # dtc_entropy : decison tree classifier based on entropy/information Gain #plotting : decision tree on information/entropy based from sklearn.tree import export_graphviz

问题 i was trying to plot the decision tree which is formed with GridSearchCV, but its giving me an Attribute error. AttributeError: 'GridSearchCV' object has no attribute 'n_features_' However if i try to plot a normal decision tree without GridSearchCv, then it successfully prints. code [decision tree without gridsearchcv] # dtc_entropy : decison tree classifier based on entropy/information Gain #plotting : decision tree on information/entropy based from sklearn.tree import export_graphviz

问题 I am using XGBRegressor to fit the model using gridsearchcv . I want to visulaize the trees. Here is the link I followed ( If duplicate) how to plot a decision tree from gridsearchcv? xgb = XGBRegressor(learning_rate=0.02, n_estimators=600,silent=True, nthread=1) folds = 5 grid = GridSearchCV(estimator=xgb, param_grid=params, scoring='neg_mean_squared_error', n_jobs=4, verbose=3 ) model=grid.fit(X_train, y_train) Approach 1: dot_data = tree.export_graphviz(model.best_estimator_, out_file=None

问题 I am using XGBRegressor to fit the model using gridsearchcv . I want to visulaize the trees. Here is the link I followed ( If duplicate) how to plot a decision tree from gridsearchcv? xgb = XGBRegressor(learning_rate=0.02, n_estimators=600,silent=True, nthread=1) folds = 5 grid = GridSearchCV(estimator=xgb, param_grid=params, scoring='neg_mean_squared_error', n_jobs=4, verbose=3 ) model=grid.fit(X_train, y_train) Approach 1: dot_data = tree.export_graphviz(model.best_estimator_, out_file=None

问题 I'm looking to visualize a regression tree built using any of the ensemble methods in scikit learn (gradientboosting regressor, random forest regressor,bagging regressor). I've looked at this question which comes close, and this question which deals with classifier trees. But these questions require the 'tree' method, which is not available to the regression models in SKLearn. but it didn't seem to yield a result. I'm running into issues because there is no .tree method for the regression