Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis

Abstract

Background: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. Results: To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. Conclusion: MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

MLcps: machine learning cumulative performance score for classification problems

“…S5 ) to train and evaluate 8 different models ( Supplementary Table S1 ) to identify the best-performing model for CLL, cervical cancer, and the TCGA datasets. For the biological signal dataset, we utilized the “customML” feature from the Machine Learning Made Easy [ 26 ] tool to train and evaluate 6 different models and identify the best-performing one for classifying alcohol consumers and nonconsumers.…”

Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning–driven data analysis

“…All supporting data, including the input dataset, “inputParameters.pkl,” and “results.pkl” files, for all evaluated datasets, are available on Zenodo [ 43 ]. The “results.pkl” files can be visualized using the Visualization feature of MLme.…”