Churn prepaid customers classified by HyperOpt techniques

Amrehn, M., Mualla, F., Angelopoulou, E., Steidl, S., & Maier, A. (2019, January 4). The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data. Retrieved from arxiv.org: https://arxiv.org/pdf/1812.08102.pdfSearch in Google Scholar

Azeem, M., Usman, M., & Fong, A. C. (2017). A churn prediction model for prepaid customers in telecom using fuzzy classifiers. New York: Springer Science+Business Media.Search in Google Scholar

Bergstra, J., Yamins, D., & Cox, D. D. (2013). Hyperopt: A Python Library for Optimizing theHyperparameters of Machine Learning Algorithms. THe 12th Python in Science Conference.10.25080/Majora-8b375195-003Search in Google Scholar

Brownlee, J. (2016, March 23). Gradient Descent For Machine Learning. Retrieved from machinelearningmastery.com: https://machinelearningmastery.com/gradient-descent-for-machine-learning/Search in Google Scholar

Brownlee, J. (2016, April 25). Machine Learning Mastery. Retrieved from Boosting and AdaBoost for Machine Learning: https://machinelearningmastery.com/boosting-and-adaboost-for-machine-learning/Search in Google Scholar

Brownlee, J. (2020, August 15). Logistic Regression for Machine Learning. Retrieved from machinelearningmastery.com: https://machinelearningmastery.com/logistic-regression-for-machine-learning/Search in Google Scholar

catboost.ai. (2020). Overview of CatBoost. Retrieved from catboost.ai: https://catboost.ai/docs/concepts/about.htmlSearch in Google Scholar

CatBoost: gradient boosting with categorical features support. (2018).Search in Google Scholar

Chawla, N. V., Hall, L. O., Bowyer, K. W., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority oversampling technique. Journal of Artificial Intelligence Research, 321-357.10.1613/jair.953Search in Google Scholar

Chawla, N. V., Lazarevic, A., Hall, L. O., & Bowyer, K. (2003). Smoteboost: Improving prediction of the minority class in boosting. Principles of Knowledge Discovery in Databases.10.1007/978-3-540-39804-2_12Search in Google Scholar

Chen, C., Liaw, A., & Breiman, L. (n.d.). statistics.berkeley. Retrieved from Using Random Forest to Learn Imbalanced Data: https://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdfSearch in Google Scholar

Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM.10.1145/2939672.2939785Search in Google Scholar

Cunningham, P., & Delany, S. J. (2007). k-Nearest Neighbour Classifiers. ResearchGate. datacamp.com. (2020, October 31). F1-Score. Retrieved from datacamp.com: https://campus.datacamp.com/courses/marketing-analytics-predicting-customer-churn-in-python/churn-prediction?ex=16Search in Google Scholar

Dorogush, A. V., Ershov, V., & Gulin, A. (2018, October 24). CatBoost: gradient boosting with categorical features. Retrieved from arxiv.org: https://arxiv.org/pdf/1810.11363.pdfSearch in Google Scholar

Drummond, C., & Holte, R. C. (2003). Class imbalance, and cost sensitivity: why under-sampling beats over-sampling. International Conference on Machine Learning.Search in Google Scholar

Fasel, I. (2001, October 23). AdaBoost. Retrieved from caseweb.ucsd.edu: https://cseweb.ucsd.edu/classes/fa01/cse291/AdaBoost.pdfSearch in Google Scholar

Freund, Y., & Schapire, R. (1996). Experiments with a new boosting algorithm. The 13th International Conference on Machine Learning, (pp. 148-156).Search in Google Scholar

G. Lemaitre, F. N. (2020). imbalanced-learn. Retrieved from Ensemble of samplers: https://imbalanced-learn.readthedocs.io/en/stable/ensemble.html geeksforgeeks.org. (2020, May 16). ML | Stochastic Gradient Descent (SGD). Retrieved from geeksforgeeks.org: https://www.geeksforgeeks.org/ml-stochastic-gradient-descent-sgd/Search in Google Scholar

Harrison, O. (2018, September 10). Towards Data Science. Retrieved from Machine Learning Basics with the K-Nearest Neighbors Algorithm: https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761Search in Google Scholar

Hulse, J. V., Khoshgoftaar, T. M., & Napolitano, A. (2007). Experimental perspectives on learning from imbalanced data. The 24th International Conference on Machine Learning, (pp. 935-942). Corvallis, OR, USA.10.1145/1273496.1273614Search in Google Scholar

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., . . . Liu, T. (2017). LightGBM: A Highly Efficient Gradient Boosting. 31st Conference on Neural Information Processing Systems. Long Beach, CA, USA.Search in Google Scholar

Kesikoglu, M. H., Atasever, U. H., Ozkan, C., & E.Besdok. (2016, July 12-19). THE USAGE OF RUSBOOST BOOSTING METHOD FOR CLASSIFICATION OF IMPERVIOUS SURFACES. Retrieved from Semantincsholar.org: https://pdfs.semanticscholar.org/98eb/e7c18ee8040f43cf4677fbf4ef8ed0a067c9.pdfSearch in Google Scholar

Kumar, N. (2020, May 15). Geeks for Geeks. Retrieved from Naive Bayes Classifiers: https://www.geeksforgeeks.org/naive-bayes-classifiers/Search in Google Scholar

LightGBM. (2020, October 30). LightGBM. Retrieved from LightGMB - Features: https://lightgbm.readthedocs.io/en/latest/Features.htmlSearch in Google Scholar

Microsoft Corporation. (2020, October 30). LightGBM, Release 3.0.0.99. Redmond, Washington, USA: Microsoft Corporation. Retrieved from What Makes LightGBM lightning fast?: https://towardsdatascience.com/what-makes-lightgbm-lightning-fast-a27cf0d9785eSearch in Google Scholar

Mihai, M. (2010, May 5). Software.ucv.ro. Retrieved from Naive-Bayes Classification Algorithm: http://software.ucv.ro/~cmihaescu/ro/teaching/AIR/docs/Lab4-NaiveBayes.pdfSearch in Google Scholar

NCSS. (2020). Logistic Regression. Retrieved from ncss-wpengine.netdna-ssl.com: https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Logistic_Regression.pdfSearch in Google Scholar

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2019, January 20). CatBoost: unbiased boosting with categorical features. Retrieved from arxiv.org: https://arxiv.org/pdf/1706.09516.pdfSearch in Google Scholar

readthedocs.io. (2020). imbalanced-learn. Retrieved from RUSBoostClassifier: https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.ensemble.RUSBoostClassifier.htmlSearch in Google Scholar

scikit-learn.org. (2020). Naive Bayes. Retrieved from scikit-learn.org: https://scikit-learn.org/stable/modules/naive_bayes.htmlSearch in Google Scholar

scikit-learn.org. (2020). Stochastic Gradient Descent. Retrieved from scikit-learn.org: https://scikit-learn.org/stable/modules/sgd.htmlSearch in Google Scholar

Seiffert, C., Khoshgoftaar, T. M., Hulse, J. V., & Napolitano, A. (2008). RUSBoost: Improving Classification Performance when Training Data is Skewed. ResearchGate.10.1109/ICPR.2008.4761297Search in Google Scholar

Seiffert, C., Khoshgoftaar, T. M., Hulse, J. V., & Napolitano, A. (2010, January 1). RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 40, NO. 1, pp. 185-197.10.1109/TSMCA.2009.2029559Search in Google Scholar

Sutton, O. (2012, February). Introduction to k Nearest Neighbour Classification and. Retrieved from Leicestermath.org.uk: http://www.leicestermath.org.uk/KNN/OliverKNN_Talk.pdfSearch in Google Scholar

Swaminathan, S. (2018, March 15). Logistic Regression — Detailed Overview. Retrieved from https://towardsdatascience.com:https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bcSearch in Google Scholar

Tu, C., Liu, H., & Xu, B. (2017). AdaBoost typical Algorithm and its application research. MATEC Web of Conferences 139, 00222. EDP Sciences.Search in Google Scholar

Weiss, G. M. (2004). Mining with rarity: A unifying framework. SIGKDD Explorations.10.1145/1007730.1007734Search in Google Scholar