Supervised machine learning is a powerful technique in artificial intelligence that allows computers to learn from labeled data and make predictions about future instances. In this article, we will explore the world of supervised machine learning, discussing the different algorithms involved, comparing their performance, and emphasizing the importance of explainability in AI.
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Understanding Supervised Machine Learning
Supervised machine learning is a specific type of machine learning that involves training algorithms on labeled data, where each data point is associated with a corresponding label or output. The objective is for the algorithm to learn patterns and relationships from the labeled data, enabling it to predict labels for new, unseen data.
Supervised machine learning can be categorized into two main types: classification and regression. Classification algorithms map input data to predefined classes or categories, while regression algorithms predict continuous real-valued outputs.
The most popular classification algorithms are the following:
Support Vector Machine (SVM)
Support Vector Machine (SVM) is a widely used and powerful supervised machine learning algorithm for classification tasks. It aims to find the optimal hyperplane that effectively separates different classes in the data space. SVM performs well even with limited training data by maximizing the margin between classes. Notably, SVM can handle high-dimensional data and is resistant to overfitting.
Random Forest
Random Forest is an ensemble learning technique that combines multiple decision trees to improve accuracy and reduce overfitting. By constructing each decision tree using a random subset of the data and features, Random Forest ensures diversity in predictions. The final classification is determined by aggregating the predictions of all decision trees. It is known for its robustness, scalability, and ability to handle large datasets with high dimensionality.
Naïve Bayes
Naïve Bayes is a simple and efficient supervised machine learning algorithm based on Bayes' theorem. Despite its simplicity, Naïve Bayes performs surprisingly well in various classification tasks, especially in text classification and spam filtering. The algorithm assumes that the features are conditionally independent given the class label, simplifying the computation of probabilities. It is particularly useful for dealing with large datasets and is relatively less affected by the curse of dimensionality.
Decision Trees
Decision trees are widely used for both classification and regression tasks. They recursively split the data based on the features to create a tree-like structure, with each node representing a decision based on a specific feature value. The leaves of the tree correspond to class labels or predicted values. Decision trees are easy to interpret and visualize, making them popular for understanding the decision-making process of the model.
Neural Networks (Perceptron)
Neural networks, inspired by the structure and functioning of the human brain, are powerful models capable of handling complex regression and classification tasks. The Perceptron is a type of neural network consisting of interconnected nodes, or neurons, organized in layers. Each neuron applies an activation function to the weighted sum of its inputs and passes the output to the next layer. Neural networks can learn intricate patterns and relationships in the data but require a large amount of training data and computational resources.
Comparing Algorithm Performance
On comparing the performance of different algorithms using small and large Diabetes datasets, Support Vector Machine (SVM) outperformed other algorithms for the large dataset, achieving a correct classification rate of 77.34%. SVM also demonstrated high precision for both positive and negative diabetes cases. Naïve Bayes, Random Forest, JRip, Neural Networks (Perceptron), Decision Table, and Decision Tree (J48) were also evaluated, but none could match the precision and accuracy of SVM.
SVM maintained its superiority with a correct classification rate of 72.92% in the case of the small dataset. Although Random Forest and Naïve Bayes showed commendable performance, they fell slightly behind SVM in terms of accuracy. This highlights the effectiveness of SVM in handling the Diabetes dataset and its potential for accurate and precise classification in real-world applications. These findings can be valuable for researchers and practitioners in the field of machine learning and healthcare, where accurate disease diagnosis is of utmost importance.
Importance of Explainability in Supervised Machine Learning
As AI and supervised machine learning models are increasingly being applied in various domains, the demand for explainable AI has grown significantly. Explainability in the context of supervised machine learning refers to the ability to interpret and understand the decisions made by AI models, providing insights into the factors influencing predictions. This is particularly crucial in domains where critical decisions are made, such as healthcare, finance, and the judicial system, as it ensures transparency and accountability in the decision-making process.
Explainable AI models in supervised machine learning offer several advantages, such as the following:
Trust and Acceptance: In sensitive domains like healthcare, patients and medical professionals need to trust the decisions made by AI models. By providing insights into why a certain prediction was made, explainable AI helps build trust and confidence in the model's capabilities.
Fairness and Bias Mitigation: AI models can inadvertently learn biases present in the training data, leading to unfair predictions. In supervised machine learning, explainable AI allows us to identify and mitigate such biases, ensuring fairness in the model's decision-making.
Regulatory Compliance: In sectors like finance and healthcare, regulations often require AI models to be explainable. This provides justifications for the models' decisions, ensuring compliance with legal and ethical standards.
Debugging and Improvement: In supervised machine learning, if an AI model makes incorrect predictions, explainability helps in identifying the root cause of the error. This facilitates model improvement and ensures better performance over time.
Domains Benefiting from Supervised Machine Learning
Medical Domain/Healthcare: Explainable AI models are of paramount importance in the healthcare industry. Medical professionals need to understand the reasons behind AI predictions to make informed decisions about patient care and treatment plans. Explainable AI can provide insights into the features influencing predictions, such as what symptoms or risk factors contribute to a particular medical diagnosis.
Banking/Financial Domain: Financial institutions often rely on AI models in supervised machine learning to make decisions related to credit approvals, risk assessment, and fraud detection. In such cases, explainability is vital to justify credit denials and understand the factors influencing a risk score. Explainable AI models provide insights into how certain financial behaviors or credit history impact the final decision, ensuring accountability and enabling customers to understand why their applications were approved or denied.
Bioinformatics: In supervised machine learning for bioinformatics, explainable AI is instrumental in advancing biomedical research and drug development. AI models help predict the potential functions of genes or proteins, and understanding the features influencing these predictions provides valuable insights into disease mechanisms and potential drug targets.
Automobile Industry: The development of autonomous vehicles relies heavily on AI algorithms in supervised machine learning to make critical decisions on the road. Explainable AI models are essential in understanding the reasons behind an autonomous vehicle's actions, especially in the event of an accident or unexpected behavior. By identifying the contributing factors, engineers can improve the safety and reliability of autonomous driving systems.
Marketing: In the field of marketing, explainable AI helps businesses understand customer preferences and improve product recommendations. Companies can tailor their marketing strategies to target specific customer segments by analyzing the features influencing a recommendation.
Judicial System: Explainable AI is critical in the legal domain for ensuring fairness and transparency in the decision-making process. Judges and legal experts need to understand the factors contributing to automated decisions, especially in areas like criminal justice, where the stakes are high. An explainable AI system can provide justifications for its recommendations, enabling judges to validate the decision and make fair judgments.
References
- Osisanwo, Babcock University, Akinsola, Awodele, Hinmikaiye, Olakanmi, & Akinjobi. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology, 48(3), 128–138. https://www.researchgate.net/publication/318338750_Supervised_Machine_Learning_Algorithms_Classification_and_Comparison
- Burkart, N., & Huber, M. F. (2021). A Survey on the Explainability of Supervised Machine Learning. Journal of Artificial Intelligence Research, 70, 245–317. DOI: https://doi.org/10.1613/jair.1.12228
- Singh, A., Nawayseh, N., Singh, H., Dhabi, Y. K., & Samuel, S. (2023). Internet of agriculture: Analyzing and predicting tractor ride comfort through supervised machine learning. Engineering Applications of Artificial Intelligence, 125, 106720. DOI: https://doi.org/10.1016/j.engappai.2023.106720