Machine Learning Toolbox
Pankaj Agarwal · Machine Learning and Applications An International Journal · 2016
This paper presents a review & performs a comparative evaluation of few known machine learning algorithms in terms of their suitability & code performance on any given data set of any size.In this paper, we describe our Machine Learning ToolBox that we have built using python programming language.The algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes, Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation).The algorithms are tested on iris and diabetes dataset and are compared on the basis of their accuracy under different conditions.However using our tool one can apply any of the implemented ML algorithms on any dataset of any size.The main goal of building a toolbox is to provide users with a platform to test their datasets on different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the data best.The toolbox allows the user to choose a dataset of his/her choice either in structured or unstructured form and then can choose the features he/she wants to use for training the machine We have given our concluding remarks on the performance of implemented algorithms based on experimental analysis.