Research of Machine Learning Algorithms using K-Fold Cross Validation
Nagadevi Darapureddy1, Nagaprakash Karatapu2, Tirumala Krishna Battula3
1Nagadevi Darapureddy, Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad (Telangana), India.
2Dr. Nagaprakash Karatapu, Department of ECE, Gudlavalleru Engineering College, Gudlavalleru (A.P), India.
3Dr. Tirumala Krishna Battula, Department of ECE, JNTU Kakinada, Kakinada (A.P), India.
Manuscript received on 15 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 06 September 2019 | PP: 215-218 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F10430886S19/19©BEIESP | DOI: 10.35940/ijeat.F1043.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In machine learning, Classification is one of the most important research area. Classification allocates the given input to a known category. In this paper different machine algorithms like Logistic regression (LR), Decision tree (DT), Support vector machine (SVM), K nearest neighbors (KNN) were implemented on UCI breast cancer dataset with preprocessing. The models were trained and tested with k-fold cross validation data. Accuracy and run time execution of each classifier are implemented in python.
Keywords: Logistic Regression (LR), Decision Tree(DT), Support Vector Machine (SVM), K Nearest Neighbors (KNN), K Fold Cross Validation.
Scope of the Article: Machine Learning