Classes |
| class | ml_lib.random_forest.DecisionTree |
| | Basic DecisionTree that the random forest is based on. More...
|
| class | ml_lib.random_forest.RFBase |
| | Base class for random forest classifier. More...
|
| class | ml_lib.random_forest.RFBreiman |
| | Train a random forest using DecisionTrees on bootstrap samples using splits on random attributes and random values on those attributes. More...
|
| class | ml_lib.random_forest.RFRandomInputSubset |
| | Train a random forest using DecisionTrees on bootstrap samples where each sample has a random subset of dimensions but the split point is performed using a minimum entropy criteria. More...
|
Namespaces |
| namespace | ml_lib::random_forest |
Functions |
| def | ml_lib::random_forest.binary_greater_than |
| def | ml_lib::random_forest.binary_less_than |
| def | ml_lib::random_forest.create_binary_tests |
| def | ml_lib::random_forest.evaluate_classifier |
| | Evaluate classifier by dividing dataset into training and test set.
|
| def | ml_lib::random_forest.load_pickle |
| def | ml_lib::random_forest.min_entropy_split |
| | Find the split that produces subsets with the minimum combined entropy return splitting attribute & splitting point for that attribute.
|
| def | ml_lib::random_forest.mode_exhaustive |
| | Helper functions.
|
| def | ml_lib::random_forest.print_separator |
| def | ml_lib::random_forest.random_subset |
| def | ml_lib::random_forest.random_subset_split |
| | splitter in decision tree
|
| def | ml_lib::random_forest.save_pickle |
| def | ml_lib::random_forest.split_random_subset |
| def | ml_lib::random_forest.totally_random_split |
Variables |
| | ml_lib::random_forest.bad = False |
| tuple | ml_lib::random_forest.data = ds.Dataset(train, resp) |
| tuple | ml_lib::random_forest.dataset = Dataset(inputs, outputs) |
| tuple | ml_lib::random_forest.dt = DecisionTree(data) |
| dictionary | ml_lib::random_forest.extra_args = {'number_of_learners': (i+1)*10} |
| tuple | ml_lib::random_forest.inputs = np.float32(iris_array[:, 0:4]) |
| tuple | ml_lib::random_forest.iris_array = np.matrix(np.loadtxt('iris.data', dtype='|S30', delimiter=',')) |
| tuple | ml_lib::random_forest.new_dt = DecisionTree() |
| list | ml_lib::random_forest.outputs = iris_array[:, 4] |
| tuple | ml_lib::random_forest.packed = dt.pack_tree() |
| tuple | ml_lib::random_forest.pred_new = new_dt.predict(test) |
| tuple | ml_lib::random_forest.pred_old = dt.predict(test) |
| list | ml_lib::random_forest.preds = [] |
| tuple | ml_lib::random_forest.resp = np.mat(np.round(np.random.rand(1, 20))) |
| tuple | ml_lib::random_forest.test = np.mat(np.random.rand(4, 1)) |
| | ml_lib::random_forest.test_iris = False |
| | ml_lib::random_forest.test_new_design = True |
| | ml_lib::random_forest.test_number_trees = False |
| | ml_lib::random_forest.test_packing = False |
| | ml_lib::random_forest.test_pca = False |
| | ml_lib::random_forest.test_pickle = False |
| tuple | ml_lib::random_forest.train = np.mat(np.random.rand(4, 20)) |
| list | ml_lib::random_forest.tree_types = [RFBreiman, RFRandomInputSubset] |