this PDF file Handwritten Digit Recognition Using Machine Learning Algorithms | Shamim | Indonesian Journal of Science and Technology 1 PB
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In this study, we assessed six different machine learning methods, including Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB), Logistic Regression
4.2 Experimental Results and Analysis We have used supervised algorithms like Decision tree, Naive Bayes, Random Forest, Support Vector Machine SVM, K-Nearest Neighbors KNN and
3.2.4 Machine Learning Algorithms: For their known accuracy rate Random Forest Algorithm, Logistic Regression, Support Vector Machine algorithm we use to applied on the training data
Then Some classic Machine Learning algorithms like Naive Bayes, Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine SVM, Decision Tree and Neural Network
I've used many a type of algorithms like Natural Language Processing NLP, Logistic Regression LR, Multinomial Naïve Bayes MNB, Support Vector Classifier SVC, Random Forest Classifier
The results of performance calculations for the K-Nearest Neighbor, Decision Tree, Random Forest, and Naïve Bayes algorithms using data validation, namely 10 k-fold and performance
Target utama penelitian yang akan dilakukan adalah mengetahui performa metode Naïve Bayes, Decision Tree, Neural Network, Random Forest, dan Suport Vector Machine dengan melihat rata
Keywords: Decision Tree; K-Nearest Neighbor; Multinomial Naïve Bayes; Neo; Oversampling; Random Forest; Sentiment Analysis; Support Vector Machine; INTRODUCTION PDDikti operators
