1mlpack_decision_stump(1) General Commands Manual mlpack_decision_stump(1)
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6 mlpack_decision_stump - decision stump
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9 mlpack_decision_stump [-h] [-v]
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12 This program implements a decision stump, which is a single-level deci‐
13 sion tree. The decision stump will split on one dimension of the input
14 data, and will split into multiple buckets. The dimension and bins are
15 selected by maximizing the information gain of the split. Optionally,
16 the minimum number of training points in each bin can be specified with
17 the --bucket_size (-b) parameter.
18 The decision stump is parameterized by a splitting dimension and a vec‐
20 tor of values that denote the splitting values of each bin.
21 This program enables several applications: a decision tree may be
23 trained or loaded, and then that decision tree may be used to classify
24 a given set of test points. The decision tree may also be saved to a
25 file for later usage.
26 To train a decision stump, training data should be passed with the
28 --training_file (-t) option, and their corresponding labels should be
29 passed with the --labels_file (-l) option. Optionally, if --labels_file
30 is not specified, the labels are assumed to be the last dimension of
31 the training dataset. The --bucket_size (-b) parameter controls the
32 minimum number of training points in each decision stump bucket.
33 For classifying a test set, a decision stump may be loaded with the
35 --input_model_file (-m) parameter (useful for the situation where a
36 stump has not just been trained), and a test set may be specified with
37 the --test_file (-T) parameter. The predicted labels will be saved to
38 the file specified with the --predictions_file (-p) parameter.
39 Because decision stumps are trained in batch, retraining does not make
41 sense and thus it is not possible to pass both --training_file and
42 --input_model_file; instead, simply build a new decision stump with the
43 training data.
44 A trained decision stump can be saved with the --output_model_file (-M)
46 option. That stump may later be re-used in subsequent calls to this
47 program (or others).
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50 --bucket_size (-b) [int]
51 The minimum number of training points in each decision stump
52 bucket. Default value 6.
53 --help (-h)
55 Default help info.
56 --info [string]
58 Get help on a specific module or option. Default value ''.
59 --input_model_file (-m) [string] File containing decision stump
60 model to load. Default value ''.
61 --labels_file (-l) [string]
63 A file containing labels for the training set.If not specified,
64 the labels are assumed to be the last row of the training data.
65 Default value ’'.
66 --test_file (-T) [string]
68 A file containing the test set. Default value ’'. --train‐
69 ing_file (-t) [string] A file containing the training set.
70 Default value ''.
71 --verbose (-v)
73 Display informational messages and the full list of parameters
74 and timers at the end of execution.
75 --version (-V)
77 Display the version of mlpack.
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80 --output_model_file (-M) [string] File to save trained decision stump
81 model to. Default value ''. --predictions_file (-p) [string] The file
82 in which the predicted labels for the test set will be written. Default
83 value ''.
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87 For further information, including relevant papers, citations, and the‐
88 ory, For further information, including relevant papers, citations, and
89 theory, consult the documentation found at http://www.mlpack.org or
90 included with your consult the documentation found at
91 http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK.
92 DISTRIBUTION OF MLPACK.
93 mlpack_decision_stump(1)