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Predictive Models: Regression, Decision Trees, and Clustering
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Abstract
This paper explores three fundamental machine learning techniques—linear regression, k-means clustering, and decision trees—and their applications in predictive modeling. In the era of data proliferation, machine learning stands at the intersection of computer science and artificial intelligence, playing a pivotal role in algorithm and model development for enhanced predictions and decision-making. The study delves into the intricacies of these techniques, starting with a focus on linear regression, a supervised learning algorithm for establishing relationships between independent and dependent variables. The process involves data preparation, exploration, feature selection, model building, and evaluation. A practical example demonstrates the application of linear regression in analyzing the relationship between income and happiness. The exploration then extends to k-means clustering, an unsupervised learning algorithm used for grouping unlabeled datasets into distinct clusters. The iterative nature of k-means involves assigning data points to clusters based on centroid proximity, contributing to efficient data exploration. A graphical representation illustrates the step-by-step process of data point grouping and centroid recalibration. The advantages of k-means, including computational efficiency and simplicity, are discussed, along with considerations such as sensitivity to initialization and the manual specification of the number of clusters. The paper concludes with an examination of decision trees, versatile algorithms used for both classification and regression tasks. Decision trees construct hierarchical structures based on features, facilitating straightforward decision-making processes. A practical example illustrates how decision trees assess credit risk based on credit history and loan term. The strengths of decision trees, such as visual representation and non-linear pattern capture, are outlined, alongside considerations like overfitting. In summary, this paper provides insights into the strengths, limitations, and applications of linear regression, k-means clustering, and decision trees. These techniques offer valuable tools in data analysis and prediction, with their effectiveness dependent on specific problem domains and datasets. The study contributes to a comprehensive understanding of these machine learning methods and suggests future research directions, including exploring advanced variations and real-world applications.
Keywords
Machine Learning, Data Science, Predicting Model, Regression, Decision Trees, K-means Clustering, Supervised Learning, Unsupervised Learning, Data Prediction, Computational Efficiency.
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Cite this article
Huang,X. (2024). Predictive Models: Regression, Decision Trees, and Clustering. Applied and Computational Engineering,79,124-133.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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