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    <p>Is there any particular reason why you're not using Python (namely the scikit-learn and multiprocessing modules) to implement this? Using joblib, I've trained random forests on datasets of similar size in a fraction of the time it takes in R. Even without multiprocessing, random forests are significantly faster in Python. Here's a quick example of training a RF classifier and cross validating in Python. You can also easily extract feature importances and visualize the trees. </p> <pre><code>import numpy as np from sklearn.metrics import * from sklearn.cross_validation import StratifiedKFold from sklearn.ensemble import RandomForestClassifier #assuming that you have read in data with headers #first column corresponds to response variable y = data[1:, 0].astype(np.float) X = data[1:, 1:].astype(np.float) cm = np.array([[0, 0], [0, 0]]) precision = np.array([]) accuracy = np.array([]) sensitivity = np.array([]) f1 = np.array([]) matthews = np.array([]) rf = RandomForestClassifier(n_estimators=100, max_features = 5, n_jobs = 2) #divide dataset into 5 "folds", where classes are equally balanced in each fold cv = StratifiedKFold(y, n_folds = 5) for i, (train, test) in enumerate(cv): classes = rf.fit(X[train], y[train]).predict(X[test]) precision = np.append(precision, (precision_score(y[test], classes))) accuracy = np.append(accuracy, (accuracy_score(y[test], classes))) sensitivity = np.append(sensitivity, (recall_score(y[test], classes))) f1 = np.append(f1, (f1_score(y[test], classes))) matthews = np.append(matthews, (matthews_corrcoef(y[test], classes))) cm = np.add(cm, (confusion_matrix(y[test], classes))) print("Accuracy: %0.2f (+/- %0.2f)" % (accuracy.mean(), accuracy.std() * 2)) print("Precision: %0.2f (+/- %0.2f)" % (precision.mean(), precision.std() * 2)) print("Sensitivity: %0.2f (+/- %0.2f)" % (sensitivity.mean(), sensitivity.std() * 2)) print("F1: %0.2f (+/- %0.2f)" % (f1.mean(), f1.std() * 2)) print("Matthews: %0.2f (+/- %0.2f)" % (matthews.mean(), matthews.std() * 2)) print(cm) </code></pre>
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    1. POSuggestions for speeding up Random Forests
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