Docstring:
Usage: qiime sample-classifier predict-regression [OPTIONS]
Use trained estimator to predict target values for new samples. These will
typically be unseen samples, e.g., test data (derived manually or from
split_table) or samples with unknown values, but can theoretically be any
samples present in a feature table that contain overlapping features with
the feature table used to train the estimator.
Inputs:
--i-table ARTIFACT FeatureTable[Frequency | RelativeFrequency |
PresenceAbsence | Composition]
Feature table containing all features that should be
used for target prediction. [required]
--i-sample-estimator ARTIFACT SampleEstimator[Regressor]
Sample regressor trained with fit_regressor. [required]
Parameters:
--p-n-jobs NTHREADS Number of jobs to run in parallel. [default: 1]
Outputs:
--o-predictions ARTIFACT SampleData[RegressorPredictions]
Predicted target values for each input sample.
[required]
Miscellaneous:
--output-dir PATH Output unspecified results to a directory
--verbose / --quiet Display verbose output to stdout and/or stderr during
execution of this action. Or silence output if
execution is successful (silence is golden).
--example-data PATH Write example data and exit.
--citations Show citations and exit.
--help Show this message and exit.
Import:
from qiime2.plugins.sample_classifier.methods import predict_regression
Docstring:
Use trained regressor to predict target values for new samples.
Use trained estimator to predict target values for new samples. These will
typically be unseen samples, e.g., test data (derived manually or from
split_table) or samples with unknown values, but can theoretically be any
samples present in a feature table that contain overlapping features with
the feature table used to train the estimator.
Parameters
----------
table : FeatureTable[Frequency | RelativeFrequency | PresenceAbsence | Composition]
Feature table containing all features that should be used for target
prediction.
sample_estimator : SampleEstimator[Regressor]
Sample regressor trained with fit_regressor.
n_jobs : Threads, optional
Number of jobs to run in parallel.
Returns
-------
predictions : SampleData[RegressorPredictions]
Predicted target values for each input sample.
