ensemble-learning

I'm using sklearn's 'predict_proba()' to predict the probability of a sample belonging to a category for each estimators in Adaboost classifier. from sklearn.ensemble import AdaBoostClassifier clf = AdaBoostClassifier(n_estimators=50) for estimator in clf.estimators_: print estimator.predict_proba(X_test) Adaboost implements its predict_proba() like this: https://github.com/scikit-learn/scikit-learn/blob/bb39b49/sklearn/ensemble/weight_boosting.py#L733 DecisionTreeClassifier is sklearn's base estimator for Adaboost classifier. DecisionTreeClassifier implements its predict_proba() like this:

I was reading a xgb notebook and the xgb.plot.tree command in example result in a pic like this: However when i do the same thing I got a pic like this which are two separate graphs and in different colors too. Is that normal? are the two graphs two trees? Jmi47 I have the same issue. According to an issue case on the xgboost github repository, this could be due to a change in the DiagrammeR library used by xgboost for rendering trees. https://github.com/dmlc/xgboost/issues/2640 Instead of modifying the dgr_graph object with diagrammeR commands, I chose to create a new version of the function

I am trying to create my first ensemble models in keras. I have 3 input values and a single output value in my dataset. from keras.optimizers import SGD,Adam from keras.layers import Dense,Merge from keras.models import Sequential model1 = Sequential() model1.add(Dense(3, input_dim=3, activation='relu')) model1.add(Dense(2, activation='relu')) model1.add(Dense(2, activation='tanh')) model1.compile(loss='mse', optimizer='Adam', metrics=['accuracy']) model2 = Sequential() model2.add(Dense(3, input_dim=3, activation='linear')) model2.add(Dense(4, activation='tanh')) model2.add(Dense(3, activation

I am using Adaboost to fit a classification problem. We can do the following: ens = fitensemble(X, Y, 'AdaBoostM1', 100, 'Tree') Now 'Tree' is the learner and we can change this to 'Discriminant' or 'KNN'. Each learner uses a certain Template Object Creation Function . More info here . Is it possible to create your own function and use it as a learner? And how? I open templateTree.m and templateKNN.m to see how MATLAB define Template Object Creation Function. function temp = templateKNN(varargin) classreg.learning.FitTemplate.catchType(varargin{:}); temp = classreg.learning.FitTemplate.make(