Interpretation of clf.predict_proba output

I'm using Scikit-learn to apply random forest classifier algorithm on my datasets. I am bit confused about output of clf.predict_proba.

Have read below explanation. But, still want to confirm as in which order the columns are arranged. i.e. array([0.1 ,0.9]) means probability of occurrence is 0.9 or 0.1?

Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix. Returns ------- p : 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_.