machine_learning.xgboost_classifier

Functions

data_handling(→ tuple)
main(→ None)
xgboost(→ xgboost.XGBClassifier)	# THIS TEST IS BROKEN!! >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0]))

Module Contents

machine_learning.xgboost_classifier.data_handling(data: dict) → tuple

>>> data_handling(({'data':'[5.1, 3.5, 1.4, 0.2]','target':([0])}))
('[5.1, 3.5, 1.4, 0.2]', [0])
>>> data_handling(
...     {'data': '[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', 'target': ([0, 0])}
... )
('[4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2]', [0, 0])

machine_learning.xgboost_classifier.main() → None

>>> main()

Url for the algorithm: https://xgboost.readthedocs.io/en/stable/ Iris type dataset is used to demonstrate algorithm.

machine_learning.xgboost_classifier.xgboost(features: numpy.ndarray, target: numpy.ndarray) → xgboost.XGBClassifier

# THIS TEST IS BROKEN!! >>> xgboost(np.array([[5.1, 3.6, 1.4, 0.2]]), np.array([0])) XGBClassifier(base_score=0.5, booster=’gbtree’, callbacks=None,

colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, gamma=0, gpu_id=-1, grow_policy=’depthwise’, importance_type=None, interaction_constraints=’’, learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1, missing=nan, monotone_constraints=’()’, n_estimators=100, n_jobs=0, num_parallel_tree=1, predictor=’auto’, random_state=0, reg_alpha=0, reg_lambda=1, …)