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* 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 --------- 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 |