forked from YovM/Algorithms
e406801f9e
* Reimplement polynomial_regression.py
Rename machine_learning/polymonial_regression.py to
machine_learning/polynomial_regression.py
Reimplement machine_learning/polynomial_regression.py using numpy
because the old original implementation was just a how-to on doing
polynomial regression using sklearn
Add detailed function documentation, doctests, and algorithm
explanation
* updating DIRECTORY.md
* Fix matrix formatting in docstrings
* Try to fix failing doctest
* Debugging failing doctest
* Fix failing doctest attempt 2
* Remove unnecessary return value descriptions in docstrings
* Readd placeholder doctest for main function
* Fix typo in algorithm description
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Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
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| .. | ||
| forecasting | ||
| local_weighted_learning | ||
| lstm | ||
| __init__.py | ||
| astar.py | ||
| data_transformations.py | ||
| decision_tree.py | ||
| dimensionality_reduction.py | ||
| gaussian_naive_bayes.py.broken.txt | ||
| gradient_boosting_regressor.py.broken.txt | ||
| gradient_descent.py | ||
| k_means_clust.py | ||
| k_nearest_neighbours.py | ||
| knn_sklearn.py | ||
| linear_discriminant_analysis.py | ||
| linear_regression.py | ||
| logistic_regression.py | ||
| multilayer_perceptron_classifier.py | ||
| polynomial_regression.py | ||
| random_forest_classifier.py.broken.txt | ||
| random_forest_regressor.py.broken.txt | ||
| scoring_functions.py | ||
| self_organizing_map.py | ||
| sequential_minimum_optimization.py | ||
| similarity_search.py | ||
| support_vector_machines.py | ||
| word_frequency_functions.py | ||
| xgboost_classifier.py | ||
| xgboost_regressor.py | ||