Neural Networks vs. Random Forests: A Practical Guide for Machine Learning Practitioners
I. Introduction Machine learning has evolved significantly, with Neural Networks (NNs) and Random Forests (RFs) being two widely used algorithms. While deep learning dominates in many areas, Random Forests often excel in structured data applications. Selecting a machine learning model depends on the analysis goal and application. A common approach is to compare models—logistic regression, […]
Fusing Machine Learning in R: Late Integration Predictive Modeling with fuseMLR
Introduction Recent technological advances have enabled the simultaneous collection of multi-omics data, i.e., different types or modalities of molecular data across various organ tissues of patients. For integrative predictive modeling, analyzing such data presents several challenges: Data Availability: Ideally, different modalities are measured in the same individuals, enabling early or intermediate integration techniques. However, real-world […]
Applying AI and ML to Business Problems
Identify and describe steps in an organized process to plan, build, and apply a machine learning model
Proven Tips for High-Performance Neural Networks for AI
Before using Neural networks, you should make sure that you need a complicated model. However, as far as you know that you need a non-linear decision boundary, a Neural Network may generally be a good choice. Neural networks are the backbone of many modern AI applications, allowing machines to learn from and make predictions based […]
Ensuring Data Privacy and Ethical Practices in AI
Summary Data privacy holds significant importance in the development and operation of AI systems. Protecting sensitive information and maintaining user data confidentiality are crucial considerations. By implementing robust measures, data privacy is upheld to safeguard personal information and respect individuals’ privacy. Ethical practices play a vital role in AI and machine learning projects. Emphasizing fairness, […]