I'm following this example on the scikit-learn website to perform a multioutput classification with a Random Forest model.
from sklearn.datasets import make_classification
from sklearn.multioutput import MultiOutputClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
import numpy as np

X, y1 = make_classification(n_samples=5, n_features=5, n_informative=2, n_classes=2, random_state=1)
y2 = shuffle(y1, random_state=1)
Y = np.vstack((y1, y2)).T

forest = RandomForestClassifier(n_estimators=10, random_state=1)
multi_target_forest = MultiOutputClassifier(forest, n_jobs=-1)
multi_target_forest.fit(X, Y).predict(X)

print(multi_target_forest.predict_proba(X))

From this predict_proba I get a 2 5x2 arrays:
[array([[ 0.8,  0.2],
       [ 0.4,  0.6],
       [ 0.8,  0.2],
       [ 0.9,  0.1],
       [ 0.4,  0.6]]), array([[ 0.6,  0.4],
       [ 0.1,  0.9],
       [ 0.2,  0.8],
       [ 0.9,  0.1],
       [ 0.9,  0.1]])]

I was really expecting a n_sample by n_classes matrix. I'm struggling to understand how this relates to the probability of the classes present.
The docs for predict_proba states:

array of shape = [n_samples, n_classes], or a list of n_outputs such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

I'm guessing I have the latter in the description, but I'm still struggling to understand how this relates to my class probabilities.
Furthermore, when I attempt to access the classes_ attribute for the forest model I get an AttributeError and this attribute does not exist on the MultiOutputClassifier. How can I relate the classes to the output?
print(forest.classes_)

AttributeError: 'RandomForestClassifier' object has no attribute 'classes_'


Assuming your target is (0,1), then the classifier would output a probability matrix of dimension (N,2). 
The first index refers to the probability that the data belong to class 0, and the second  refers to the probability that the data belong to class 1. 

These two would sum to 1.

You can then output the result by:

probability_class_1 = model.predict_proba(X)[:, 1]


If you have k classes, the output would be (N,k), you would have to specify the probability of which class you want.
