Getting Scikit-Learn RandomForestClassifier to output Top N results
As there isn't too much code/context you're giving us I assume that you're working with a binary classification problem:

import numpy as np
X = ... # input for classification, shape (n_samples, n_features)
y_pred = rf.predict_proba(X)[:, 1] # index slicing to retrieve a 1d-array of probabilities
y_pred
# array([  0.18, 0.21, 0.1, 0.2, 0.3])

# Now lets see what the 2 biggest probabilities are
np.argsort(y_pred)
# [2, 0, 3, 1, 4] =&gt; lowest probability is at index 2 (the 3rd probability as classified by the RandomForestClassifier)
# Now lets see what the top 5 samples look like (remember, argsort sorts in ascending order, so we take the last 5 indices of argsort):
X[np.argsort(y_pred)[-5:]]


Now you should see the rows in X with the 5 most confident predictions.