How To Install python-niaaml-doc on Fedora 36

In this tutorial we learn how to install python-niaaml-doc in Fedora 36. python-niaaml-doc is Python automated machine learning framework

Introduction

In this tutorial we learn how to install python-niaaml-doc on Fedora 36.

What is python-niaaml-doc

Documentation for python-niaaml.

We can use yum or dnf to install python-niaaml-doc on Fedora 36. In this tutorial we discuss both methods but you only need to choose one of method to install python-niaaml-doc.

Install python-niaaml-doc on Fedora 36 Using dnf

Update yum database with dnf using the following command.

sudo dnf makecache --refresh

After updating yum database, We can install python-niaaml-doc using dnf by running the following command:

sudo dnf -y install python-niaaml-doc

Install python-niaaml-doc on Fedora 36 Using yum

Update yum database with yum using the following command.

sudo yum makecache --refresh

After updating yum database, We can install python-niaaml-doc using yum by running the following command:

sudo yum -y install python-niaaml-doc

How To Uninstall python-niaaml-doc on Fedora 36

To uninstall only the python-niaaml-doc package we can use the following command:

sudo dnf remove python-niaaml-doc

python-niaaml-doc Package Contents on Fedora 36

/usr/share/doc/python-niaaml-doc
/usr/share/doc/python-niaaml-doc/CITATION.md
/usr/share/doc/python-niaaml-doc/CODE_OF_CONDUCT.md
/usr/share/doc/python-niaaml-doc/CONTRIBUTING.md
/usr/share/doc/python-niaaml-doc/examples
/usr/share/doc/python-niaaml-doc/examples/classifier.py
/usr/share/doc/python-niaaml-doc/examples/example_files
/usr/share/doc/python-niaaml-doc/examples/example_files/dataset.csv
/usr/share/doc/python-niaaml-doc/examples/example_files/dataset_categorical.csv
/usr/share/doc/python-niaaml-doc/examples/example_files/dataset_categorical_missing.csv
/usr/share/doc/python-niaaml-doc/examples/example_files/pipeline.ppln
/usr/share/doc/python-niaaml-doc/examples/export_pipeline_object.py
/usr/share/doc/python-niaaml-doc/examples/export_pipeline_text.py
/usr/share/doc/python-niaaml-doc/examples/factories.py
/usr/share/doc/python-niaaml-doc/examples/feature_encoding.py
/usr/share/doc/python-niaaml-doc/examples/feature_imputing.py
/usr/share/doc/python-niaaml-doc/examples/feature_selection.py
/usr/share/doc/python-niaaml-doc/examples/feature_selection_threshold_algorithms.py
/usr/share/doc/python-niaaml-doc/examples/feature_transform.py
/usr/share/doc/python-niaaml-doc/examples/fitness.py
/usr/share/doc/python-niaaml-doc/examples/load_data_basic.py
/usr/share/doc/python-niaaml-doc/examples/load_data_csv.py
/usr/share/doc/python-niaaml-doc/examples/load_pipeline_object_file.py
/usr/share/doc/python-niaaml-doc/examples/logger.py
/usr/share/doc/python-niaaml-doc/examples/optimization_stats.py
/usr/share/doc/python-niaaml-doc/examples/optimize_run_pipeline.py
/usr/share/doc/python-niaaml-doc/examples/optimize_run_pipeline_categorical_features.py
/usr/share/doc/python-niaaml-doc/examples/optimize_run_pipeline_logger.py
/usr/share/doc/python-niaaml-doc/examples/optimize_run_pipeline_missing_values.py
/usr/share/doc/python-niaaml-doc/examples/run_all.bat
/usr/share/doc/python-niaaml-doc/examples/run_all.sh
/usr/share/doc/python-niaaml-doc/examples/run_pipeline_optimizer_array_data.py
/usr/share/doc/python-niaaml-doc/examples/run_pipeline_optimizer_csv_data.py
/usr/share/doc/python-niaaml-doc/examples/run_pipeline_optimizer_csv_data_categorical.py
/usr/share/doc/python-niaaml-doc/examples/run_pipeline_optimizer_csv_data_missing.py
/usr/share/doc/python-niaaml-doc/examples/run_pipeline_optimizer_csv_data_v1.py
/usr/share/doc/python-niaaml-doc/niaaml.pdf
/usr/share/doc/python-niaaml-doc/paper
/usr/share/doc/python-niaaml-doc/paper/niaamlFlow.png
/usr/share/doc/python-niaaml-doc/paper/paper.bib
/usr/share/doc/python-niaaml-doc/paper/paper.md
/usr/share/licenses/python-niaaml-doc
/usr/share/licenses/python-niaaml-doc/LICENSE

References

Summary

In this tutorial we learn how to install python-niaaml-doc on Fedora 36 using yum and [dnf]((/fedora/36/dnf/).