mlpack_det(1)

✨ mlpack_det ✨

okay bestie, welcome to mlpack_det 💖 this is the User Commands-flavored page for it, and it's literally all about density estimation with density estimation trees. no cap. it does a lot of stuff related to Density Estimation Trees, and we're gonna go through every single crumb of it together 🫶

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🌸 SYNOPSIS 🌸

mlpack_det [-f int] [-m unknown] [-L int] [-l int] [-p string] [-s bool] [-T unknown] [-t unknown] [-V bool] [-M unknown] [-c string] [-g string] [-E unknown] [-e unknown] [-i unknown] [-h -v]

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💅 DESCRIPTION 💅

okay so quick recap of the tea: this program performs a number of functions related to Density Estimation Trees. The optimal Density Estimation Tree (DET) can be trained on a set of data — you pass that in with --training_file (-t) — using cross-validation, and you control how many folds with the --folds (-f) parameter. Once that trained density estimation tree exists, you can save it with the --output_model_file (-M) output parameter. periodt. ✨

The variable importances (aka the feature importance values for each dimension, iykyk) may be saved with the --vi_file (-i) output parameter 🌈 and the density estimates for each training point may be saved with the --training_set_estimates_file (-e) output parameter. chef's kiss.

Enabling path printing for each node outputs the path from the root node to a leaf for each entry in the test set, or the training set (if a test set is not provided) 🦋. Strings like LRLRLR will be output — and if you're wondering what that even means: it indicates that traversal went to the left child, then the right child, then the left child, and so forth. If lr-id or id-lr are given as the --path_format (-p) parameter, then the ID (tag) of every node along the path will be printed after or before the L or R character indicating the direction of traversal, respectively. cute, right? ✨

This program also can provide density estimates for a set of test points, specified in the --test_file (-T) parameter 💜. The density estimation tree used for this task will be the tree that was trained on the given training points, or a tree given as the parameter --input_model_file (-m). The density estimates for the test points may be saved using the --test_set_estimates_file (-E) output parameter 🌸.

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🎀 OPTIONAL INPUT OPTIONS 🎀

• --folds (-f) [int] — the number of folds of cross-validation to perform for the estimation (0 is LOOCV). Default value 10. lowkey obsessed with LOOCV.
• --help (-h) [bool] — default help info. the "help me bestie" button fr fr.
• --info [string] — print help on a specific option. Default value ''. 🧚
• --input_model_file (-m) [unknown] — trained density estimation tree to load. bring your own tree 🎀
• --max_leaf_size (-L) [int] — the maximum size of a leaf in the unpruned, fully grown DET. Default value 10. 👑
• --min_leaf_size (-l) [int] — the minimum size of a leaf in the unpruned, fully grown DET. Default value 5. ✨
• --path_format (-p) [string] — the format of path printing: 'lr', 'id-lr', or 'lr-id'. Default value 'lr'. 🌈
• --skip_pruning (-s) [bool] — whether to bypass the pruning process and output the unpruned tree only. no trimming, no regrets 💅
• --test_file (-T) [unknown] — a set of test points to estimate the density of.
• --training_file (-t) [unknown] — the data set on which to build a density estimation tree. the main character's origin story fr 🌟
• --verbose (-v) [bool] — display informational messages and the full list of parameters and timers at the end of execution. spill everything 😭
• --version (-V) [bool] — display the version of mlpack.

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💜 OPTIONAL OUTPUT OPTIONS 💜

• --output_model_file (-M) [unknown] — output to save trained density estimation tree to. 🎀
• --tag_counters_file (-c) [string] — the file to output the number of points that went to each leaf. Default value ''.
• --tag_file (-g) [string] — the file to output the tags (and possibly paths) for each sample in the test set. Default value ''.
• --test_set_estimates_file (-E) [unknown] — the output estimates on the test set from the final optimally pruned tree. ✨
• --training_set_estimates_file (-e) [unknown] — the output density estimates on the training set from the final optimally pruned tree. 🔥
• --vi_file (-i) [unknown] — the output variable importance values for each feature. 🌸

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🦋 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. go read it bestie, it's giving academia 💖

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and that's the whole page, bestie!! no notes, we ate 💅✨

okay so this masterpiece is from mlpack-4.8.0, dated 22 June 2026 🎀 mwah, slay, bye 🫶🌟