mlpack_decision_stump(1)

✨ mlpack_decision_stump ✨

okay bestie, gather round, because today we're talking about mlpack_decision_stump — it's a User Commands manual page, it lives in the mlpack family, and it's literally a decision stump and nothing else. no cap. 💖

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🌸 NAME 🌸

mlpack_decision_stump — decision stump. that's it. that's the name. iconic behavior honestly.

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🦋 SYNOPSIS 🦋

mlpack_decision_stump [-b int] [-m unknown] [-l string] [-T string] [-t string] [-V bool] [-M unknown] [-p string] [-h -v]

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💅 DESCRIPTION 💅

so this program implements a decision stump, which is a single-level decision tree. yes, one level. it's a tree that said "i'm not doing the whole growing thing, i'm doing one split and then i'm going to bed." lowkey relatable.

the decision stump will split on one dimension of the input data, and it will split into multiple buckets. the dimension and the bins are selected by maximizing the information gain of the split — so it's not just vibing, it's actually optimizing. 🎀

optionally, you can specify the minimum number of training points in each bin with the --bucket_size (-b) parameter. slay.

the decision stump is parameterized by a splitting dimension and a vector of values that denote the splitting values of each bin.

this program enables several applications, bestie, and they're all important:

• a decision tree may be trained or loaded, and then that decision tree may be used to classify a given set of test points.
• the decision tree may also be saved to a file for later usage.

To train a decision stump, training data should be passed with the --training_file (-t) parameter, and their corresponding labels should be passed with the --labels_file (-l) option. optionally, if --labels_file (-l) is not specified, the labels are assumed to be the last dimension of the training dataset. the --bucket_size (-b) parameter controls the minimum number of training points in each decision stump bucket. 🍓

for classifying a test set, a decision stump may be loaded with the --input_model_file (-m) parameter (super useful for the situation where a stump has already been trained, like, it already did the work, don't make it do it again), and a test set may be specified with the --test_file (-T) parameter. the predicted labels can be saved with the --predictions_file (-p) output parameter. ✨

🫶 important vibe check: because decision stumps are trained in batch, retraining does not make sense, and thus it is not possible to pass both --training_file (-t) and --input_model_file (-m). instead, simply build a new decision stump with the training data. pick a lane, bestie.

after training, a decision stump can be saved with the --output_model_file (-M) output parameter. that stump may later be re-used in subsequent calls to this program (or others). it's giving reusable icon. 👑

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🎀 OPTIONAL INPUT OPTIONS 🎀

• --bucket_size (-b) [int] — the minimum number of training points in each decision stump bucket. Default value 6.
• --help (-h) [bool] — default help info.
• --info [string] — print help on a specific option. Default value ''.
• --input_model_file (-m) [unknown] — decision stump model to load.
• --labels_file (-l) [string] — labels for the training set. if not specified, the labels are assumed to be the last row of the training data.
• --test_file (-T) [string] — a dataset to calculate predictions for.
• --training_file (-t) [string] — the dataset to train on.
• --verbose (-v) [bool] — display informational messages and the full list of parameters and timers at the end of execution. very "tell me everything" of it. 🙈
• --version (-V) [bool] — display the version of mlpack.

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🌈 OPTIONAL OUTPUT OPTIONS 🌈

• --output_model_file (-M) [unknown] — output decision stump model to save.
• --predictions_file (-p) [string] — the output matrix that will hold the predicted labels for the test set.

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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 good for you. 📚

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and that's the whole page, periodt. this was mlpack-3.4.2, dated 11 April 2022, and it's officially a wrap — mwah, love u, go train a stump. 💖✨🎀🦋🌸