mlpack_adaboost(1)

💖 mlpack_adaboost — the manual, but make it make sense 💖

ok bestie, so mlpack_adaboost is the mlpack_adaboost(1) command — a User Command, one of the mlpack crew — and she is literally here to serve you adaptive boosting realness. no cap. let's go 🦋

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✨ SYNOPSIS ✨

mlpack_adaboost [-m unknown] [-i int] [-l unknown] [-T unknown] [-e double] [-t unknown] [-V bool] [-w string] [-M unknown] [-P unknown] [-p unknown] [-h -v]

that's the whole vibe in one line. keep scrolling for what every single one of those means 💅

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🌸 DESCRIPTION 🌸

So this program implements the AdaBoost algorithm — aka Adaptive Boosting, she's a legend 👑. The specific variant living in here is AdaBoost.MH. It takes a weak learner — either decision stumps or perceptrons, you pick — and over many iterations it cooks up a strong learner that's a weighted ensemble of all those little weak ones. iconic behavior honestly. It keeps running those iterations until a tolerance value is crossed for change in the value of the weighted training error. periodt.

For more info on the algorithm itself, go read the paper "Improved Boosting Algorithms Using Confidence-Rated Predictions", by R.E. Schapire and Y. Singer. yes bestie, citations, we love to cite 📚✨

This program lets you train an AdaBoost model, and then apply that model to a test dataset. Two paths, main character energy either way:

• Training from scratch — you gotta pass a dataset with the --training_file (-t) option. Labels can come in with the --labels_file (-l) option; and if you don't specify labels, they'll just be assumed to be the last column of the input dataset. lowkey convenient ngl.
• Loading a model — alternately, you can load an existing AdaBoost model with the --input_model_file (-m) option.

Once a model is trained or loaded, it's ready to spit out class predictions for a given test dataset 🎀. A test dataset can be specified with the --test_file (-T) parameter. The predicted classes for each point in the test dataset get output to the --predictions_file (-P) output parameter. And the AdaBoost model itself gets output to the --output_model_file (-M) output parameter. so many files, so little time 😭

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🧚 example time, because reading is hard and examples are everything 🧚

Say you wanna run AdaBoost on an input dataset data.csv with labels labels.csv and perceptrons as the weak learner type, storing the trained model in model.bin. You'd do:

$ mlpack_adaboost --training_file data.csv --labels_file labels.csv
--output_model_file model.bin --weak_learner perceptron

and similarly, an already-trained model in model.bin can be used to provide class predictions from test data test_data.csv and store the output in predictions.csv like this:

$ mlpack_adaboost --input_model_file model.bin --test_file test_data.csv
--predictions_file predictions.csv

slay. you did that 💅

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🎀 OPTIONAL INPUT OPTIONS 🎀

one bullet per option, every single one accounted for, no skips, we don't gatekeep here 🫶

• --help (-h) [bool] — Default help info. the classic "help me please" move.
• --info [string] — Print help on a specific option. Default value ''. yes bestie, you can ask about ONE option specifically, very targeted of you ✨
• --input_model_file (-m) [unknown] — Input AdaBoost model. the glow-up you already trained.
• --iterations (-i) [int] — The maximum number of boosting iterations to be run (0 will run until convergence.) Default value 1000. so 0 means "just keep going until it settles," the commitment issues option 🔥
• --labels_file (-l) [unknown] — Labels for the training set. 🍓
• --test_file (-T) [unknown] — Test dataset. the ~test~ bestie, we love to be evaluated.
• --tolerance (-e) [double] — The tolerance for change in values of the weighted error during training. Default value 1e-10. tiny number, big responsibility 💀
• --training_file (-t) [unknown] — Dataset for training AdaBoost. your whole origin story right here 🌈
• --verbose (-v) [bool] — Display informational messages and the full list of parameters and timers at the end of execution. for when you wanna know everything that happened 🙈
• --version (-V) [bool] — Display the version of mlpack. identity check ✅
• --weak_learner (-w) [string] — The type of weak learner to use: 'decision_stump', or 'perceptron'. Default value 'decision_stump'. pick your fighter 🥊

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💜 OPTIONAL OUTPUT OPTIONS 💜

what you get back, bestie. the receipts.

• --output_model_file (-M) [unknown] — Output trained AdaBoost model. your finished masterpiece, saved for later 🎀
• --predictions_file (-P) [unknown] — Predicted labels for the test set. the answers, the tea ☕
• --probabilities_file (-p) [unknown] — Predicted class probabilities for each point in the test set. not just what, but how sure — the confidence, the self-assurance ✨

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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, she's thorough 📖💖

quick vibe check reminder: every flag, default, value and file path above is doing exactly what it says. don't freestyle the options, bestie — the defaults are there for a reason 🫶

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okay that's the whole page, you're officially an AdaBoost girlie now 💅✨ this was mlpack-4.8.0, dated 22 June 2026 — no cap, go train something iconic 👑💖