Lianne & Justin @ Just into Data liannewriting

We make Data Science SIMPLER for You! We post the codes used in the blog for your convenience. Check out our articles on justintodata.com/blog/.

liannewriting / plot-feature-importance.py

Created November 30, 2022 15:26

xgboost python machine learning

	from xgboost import plot_importance

	xgboost_step = opt.best_estimator_.steps[1]
	xgboost_model = xgboost_step[1]
	plot_importance(xgboost_model)

liannewriting / print-best-estimator.py

Created November 30, 2022 15:25

xgboost python machine learning

opt.best_estimator_.steps

liannewriting / predict-probability.py

Last active December 5, 2022 16:19

xgboost python machine learning

liannewriting / evaluate-score.py

Last active November 30, 2022 19:41

xgboost python machine learning

liannewriting / print-best-estimator.py

Created November 30, 2022 15:23

xgboost python machine learning

opt.best_estimator_

liannewriting / fit_xgboost.py

Created November 30, 2022 15:22

xgboost python machine learning

opt.fit(X_train, y_train)

liannewriting / set-up-hyperparameter-tuning.py

Last active December 6, 2022 16:30

xgboost python machine learning

	from skopt import BayesSearchCV
	from skopt.space import Real, Categorical, Integer

	search_space = {
	'clf__max_depth': Integer(2,8),
	'clf__learning_rate': Real(0.001, 1.0, prior='log-uniform'),
	'clf__subsample': Real(0.5, 1.0),
	'clf__colsample_bytree': Real(0.5, 1.0),
	'clf__colsample_bylevel': Real(0.5, 1.0),
	'clf__colsample_bynode' : Real(0.5, 1.0),

liannewriting / set-up-pipeline.py

Last active December 7, 2022 15:54

xgboost python machine learning

	from sklearn.pipeline import Pipeline
	from category_encoders.target_encoder import TargetEncoder
	from xgboost import XGBClassifier

	estimators = [
	('encoder', TargetEncoder()),
	('clf', XGBClassifier(random_state=8)) # can customize objective function with the objective parameter
	]
	pipe = Pipeline(estimators)
	pipe

liannewriting / split_train_test.py

Last active December 1, 2022 00:01

xgboost python machine learning

	from sklearn.model_selection import train_test_split

	X = df.drop(columns='result')
	y = df['result']

	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=8)

liannewriting / explore_data.py

Created November 30, 2022 15:05

xgboost python machine learning