mlpack_random_forest(1)

📦
version 4.8.0 · 5 versions
, in ubuntu stonking

🦋 NAME 🦋

mlpack_random_forest — random forests, bestie 💖

(and yes, you are reading the mlpack_random_forest(1) manual page, the user commands one, hi hello welcome, pull up a chair 🫶)

✨ SYNOPSIS ✨

mlpack_random_forest [-m unknown] [-l unknown] [-D int] [-g double] [-n int] [-N int] [-a bool] [-s int] [-d int] [-T unknown] [-L unknown] [-t unknown] [-V bool] [-w bool] [-M unknown] [-p unknown] [-P unknown] [-h -v]

💖 DESCRIPTION 💖

okay so. This program is literally an implementation of the standard random forest classification algorithm, and it's by Leo Breiman, an absolute icon, no cap 👑. A random forest can be trained and saved for later use, OR a random forest may be loaded and then it can generate predictions or class probabilities for points. Both things. It's giving versatility. 💅

The training set and its associated labels are specified with the --training_file (-t) and --labels_file (-l) parameters, respectively. The labels should be in the range [0, num_classes - 1]. And optionally, if --labels_file (-l) is not specified, the labels are assumed to be the last dimension of the training dataset. Sneaky but iconic.

When a model is trained, the --output_model_file (-M) output parameter may be used to save the trained model 🎀. A model may be loaded for predictions with the --input_model_file (-m) parameter.

🚨 bestie listen. The --input_model_file (-m) parameter may not be specified when the --training_file (-t) parameter is specified. Pick a lane. Not both.

The --minimum_leaf_size (-n) parameter specifies the minimum number of training points that must fall into each leaf for it to be split. The --num_trees (-N) controls the number of trees in the random forest 🌳🌳🌳. The --minimum_gain_split (-g) parameter controls the minimum required gain for a decision tree node to split — larger values will force higher-confidence splits. The --maximum_depth (-D) parameter specifies the maximum depth of the tree. The --subspace_dim (-d) parameter is used to control the number of random dimensions chosen for an individual node's split. And if --print_training_accuracy (-a) is specified, the calculated accuracy on the training set will be printed. ✨

Test data may be specified with the --test_file (-T) parameter, and if performance measures are desired for that test set, labels for the test points may be specified with the --test_labels_file (-L) parameter. Predictions for each test point may be saved via the --predictions_file (-p) output parameter. Class probabilities for each prediction may be saved with the --probabilities_file (-P) output parameter 💜.

🌸 the example era 🌸

For example, to train a random forest with a minimum leaf size of 20 using 10 trees on the dataset contained in data.csv with labels labels.csv, saving the output random forest to rf_model.bin and printing the training error, one could call:

$ mlpack_random_forest --training_file data.csv --labels_file labels.csv --minimum_leaf_size 20 --num_trees 10 --output_model_file rf_model.bin --print_training_accuracy

Then, to use that model to classify points in test_set.csv and print the test error given the labels test_labels.csv using that model, while saving the predictions for each point to predictions.csv, one could call:

$ mlpack_random_forest --input_model_file rf_model.bin --test_file test_set.csv --test_labels_file test_labels.csv --predictions_file predictions.csv

ate. 🍓

🎀 OPTIONAL INPUT OPTIONS 🎀

  • --help (-h) [bool] — Default help info. The classic. The moment. 💅
  • --info [string] — Print help on a specific option. Default value ''.
  • --input_model_file (-m) [unknown] — Pre-trained random forest to use for classification. Your glowed-up, already-trained bestie 🧚
  • --labels_file (-l) [unknown] — Labels for training dataset.
  • --maximum_depth (-D) [int] — Maximum depth of the tree (0 means no limit). Default value 0.
  • --minimum_gain_split (-g) [double] — Minimum gain needed to make a split when building a tree. Default value 0.
  • --minimum_leaf_size (-n) [int] — Minimum number of points in each leaf node. Default value 1.
  • --num_trees (-N) [int] — Number of trees in the random forest. Default value 10.
  • --print_training_accuracy (-a) [bool] — If set, then the accuracy of the model on the training set will be predicted (verbose must also be specified 👀).
  • --seed (-s) [int] — Random seed. If 0, std::time(NULL) is used. Default value 0.
  • --subspace_dim (-d) [int] — Dimensionality of random subspace to use for each split. 0 will autoselect the square root of data dimensionality. Default value 0.
  • --test_file (-T) [unknown] — Test dataset to produce predictions for.
  • --test_labels_file (-L) [unknown] — Test dataset labels, if accuracy calculation is desired.
  • --training_file (-t) [unknown] — Training dataset.
  • --verbose (-v) [bool] — Display informational messages and the full list of parameters and timers at the end of execution. The receipts, fr fr 📋
  • --version (-V) [bool] — Display the version of mlpack.
  • --warm_start (-w) [bool] — If true and passed along with training and input_model then trains more trees on top of existing model. Growth mindset, literally 🌱

💜 OPTIONAL OUTPUT OPTIONS 💜

  • --output_model_file (-M) [unknown] — Model to save trained random forest to. Save your work bestie, please 😭
  • --predictions_file (-p) [unknown] — Predicted classes for each point in the test set.
  • --probabilities_file (-P) [unknown] — Predicted class probabilities for each point in the test set.

🌈 ADDITIONAL INFORMATION 🌈

For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack. iykyk 📚✨

okay that's the whole page, you're welcome, go train a forest 🌳🫶. this was mlpack-4.8.0, dated 22 June 2026 — mwah, slay, bye bestie 💅💖🎀

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