def allClassifierPredictions(kidCapsule):
decisionTree = DecisionTreeClassifier(max_depth=None, min_samples_split=1, random_state=0)
randomForest = RandomForestClassifier(n_estimators=50, max_depth=None, min_samples_split=1, random_state=0)
extraTrees = ExtraTreesClassifier(n_estimators=50, max_depth=None, min_samples_split=1, random_state=0)
gradientBoost = GradientBoostingClassifier(n_estimators=50, max_depth=1, learn_rate=1.0, random_state=0)
decisionTree.compute_importances = True
randomForest.compute_importances = True
extraTrees.compute_importances = True
gradientBoost.compute_importances = True
decisionTree.fit(kidCapsule.train_M, kidCapsule.trainLabels)
randomForest.fit(kidCapsule.train_M, kidCapsule.trainLabels)
extraTrees.fit(kidCapsule.train_M, kidCapsule.trainLabels)
gradientBoost.fit(kidCapsule.train_M, kidCapsule.trainLabels)
print decisionTree.feature_importances_
print randomForest.feature_importances_
print extraTrees.feature_importances_
print gradientBoost.feature_importances_
dt_pred = decisionTree.predict(kidCapsule.M)
rf_pred = randomForest.predict(kidCapsule.M)
et_pred = extraTrees.predict(kidCapsule.M)
gb_pred = gradientBoost.predict(kidCapsule.M)
#import pdb; pdb.set_trace()
return dt_pred, rf_pred, et_pred, gb_pred
