random_forest_main.cpp File Reference

#include <mlpack/core.hpp>
#include <mlpack/methods/random_forest/random_forest.hpp>
#include <mlpack/methods/decision_tree/random_dimension_select.hpp>
#include <mlpack/core/util/mlpack_main.hpp>

Include dependency graph for random_forest_main.cpp:

This graph shows which files directly or indirectly include this file:

Classes
class	RandomForestModel
	This is the class that we will serialize. More...

Functions
	BINDING_NAME ("Random forests")
	BINDING_SHORT_DESC ("An implementation of the standard random forest algorithm by Leo Breiman " "for classification. Given labeled data, a random forest can be trained " "and saved for future use; or, a pre-trained random forest can be used for " "classification.")
	BINDING_LONG_DESC ("This program is an implementation of the standard random forest " "classification algorithm by Leo Breiman. A random forest can be " "trained and saved for later use, or a random forest may be loaded " "and predictions or class probabilities for points may be generated." "\" "The training set and associated labels are specified with the "+PRINT_PARAM_STRING("training")+" and "+PRINT_PARAM_STRING("labels")+" parameters, respectively. The labels should be in the range [0, " "num_classes - 1]. Optionally, if "+PRINT_PARAM_STRING("labels")+" is not specified, the labels are assumed " "to be the last dimension of the training dataset." "\" "When a model is trained, the "+PRINT_PARAM_STRING("output_model")+" " "output parameter may be used to save the trained model. A model may be " "loaded for predictions with the "+PRINT_PARAM_STRING("input_model")+"parameter. The "+PRINT_PARAM_STRING("input_model")+" parameter may " "not be specified when the "+PRINT_PARAM_STRING("training")+" parameter" " is specified. The "+PRINT_PARAM_STRING("minimum_leaf_size")+" parameter specifies the minimum number of training points that must fall " "into each leaf for it to be split. The "+PRINT_PARAM_STRING("num_trees")+" controls the number of trees in the random forest. The "+PRINT_PARAM_STRING("minimum_gain_split")+" parameter controls the minimum" " required gain for a decision tree node to split. Larger values will " "force higher-confidence splits. The "+PRINT_PARAM_STRING("maximum_depth")+" parameter specifies " "the maximum depth of the tree. The "+PRINT_PARAM_STRING("subspace_dim")+" parameter is used to control the " "number of random dimensions chosen for an individual node's split. If "+PRINT_PARAM_STRING("print_training_accuracy")+" is specified, the " "calculated accuracy on the training set will be printed." "\" "Test data may be specified with the "+PRINT_PARAM_STRING("test")+" " "parameter, and if performance measures are desired for that test set, " "labels for the test points may be specified with the "+PRINT_PARAM_STRING("test_labels")+" parameter. Predictions for each " "test point may be saved via the "+PRINT_PARAM_STRING("predictions")+"output parameter. Class probabilities for each prediction may be saved " "with the "+PRINT_PARAM_STRING("probabilities")+" output parameter.")
	BINDING_EXAMPLE ("For example, to train a random forest with a minimum leaf size of 20 " "using 10 trees on the dataset contained in "+PRINT_DATASET("data")+"with labels "+PRINT_DATASET("labels")+", saving the output random " "forest to "+PRINT_MODEL("rf_model")+" and printing the training " "error, one could call" "\"+PRINT_CALL("random_forest", "training", "data", "labels", "labels", "minimum_leaf_size", 20, "num_trees", 10, "output_model", "rf_model", "print_training_accuracy", true)+"\" "Then, to use that model to classify points in "+PRINT_DATASET("test_set")+" and print the test error given the labels "+PRINT_DATASET("test_labels")+" using that model, while saving the " "predictions for each point to "+PRINT_DATASET("predictions")+", one " "could call " "\"+PRINT_CALL("random_forest", "input_model", "rf_model", "test", "test_set", "test_labels", "test_labels", "predictions", "predictions"))
	BINDING_SEE_ALSO ("@decision_tree", "#decision_tree")
	BINDING_SEE_ALSO ("@hoeffding_tree", "#hoeffding_tree")
	BINDING_SEE_ALSO ("@softmax_regression", "#softmax_regression")
	BINDING_SEE_ALSO ("Random forest on Wikipedia", "https://en.wikipedia.org/wiki/Random_forest")
	BINDING_SEE_ALSO ("Random forests (pdf)", "https://link.springer.com/content/pdf/10.1023/A:1010933404324.pdf")
	BINDING_SEE_ALSO ("mlpack::tree::RandomForest C++ class documentation", "@doxygen/classmlpack_1_1tree_1_1RandomForest.html")
	PARAM_MATRIX_IN ("training", "Training dataset.", "t")
	PARAM_UROW_IN ("labels", "Labels for training dataset.", "l")
	PARAM_MATRIX_IN ("test", "Test dataset to produce predictions for.", "T")
	PARAM_UROW_IN ("test_labels", "Test dataset labels, if accuracy calculation is " "desired.", "L")
	PARAM_FLAG ("print_training_accuracy", "If set, then the accuracy of the model " "on the training set will be predicted (verbose must also be specified).", "a")
	PARAM_INT_IN ("num_trees", "Number of trees in the random forest.", "N", 10)
	PARAM_INT_IN ("minimum_leaf_size", "Minimum number of points in each leaf " "node.", "n", 1)
	PARAM_INT_IN ("maximum_depth", "Maximum depth of the tree (0 means no limit).", "D", 0)
	PARAM_MATRIX_OUT ("probabilities", "Predicted class probabilities for each " "point in the test set.", "P")
	PARAM_UROW_OUT ("predictions", "Predicted classes for each point in the test " "set.", "p")
	PARAM_DOUBLE_IN ("minimum_gain_split", "Minimum gain needed to make a split " "when building a tree.", "g", 0)
	PARAM_INT_IN ("subspace_dim", "Dimensionality of random subspace to use for " "each split. '0' will autoselect the square root of data dimensionality.", "d", 0)
	PARAM_INT_IN ("seed", "Random seed. If 0, 'std::time(NULL)' is used.", "s", 0)
	PARAM_FLAG ("warm_start", "If true and passed along with `training` and " "`input_model` then trains more trees on top of existing model.", "w")
	PARAM_MODEL_IN (RandomForestModel, "input_model", "Pre-trained random forest to " "use for classification.", "m")
	PARAM_MODEL_OUT (RandomForestModel, "output_model", "Model to save trained " "random forest to.", "M")

Detailed Description

Author

Ryan Curtin

A program to build and evaluate random forests.

mlpack is free software; you may redistribute it and/or modify it under the terms of the 3-clause BSD license. You should have received a copy of the 3-clause BSD license along with mlpack. If not, see http://www.opensource.org/licenses/BSD-3-Clause for more information.