Researchers have developed a new artificial intelligence model called AstroPT that can learn meaningful information about galaxies just by looking at images.
Category: Machine Learning
A comparative approach to understand K-Means, Hierarchical, and DBSCAN
Comparative Insights
K-Means: Best for situations where you expect clusters to be roughly spherical and have a prior sense of the number of clusters, like customer segmentation.
Hierarchical Clustering: Ideal for understanding complex, nested structures in data without needing to predefine the number of clusters, as in gene expression analysis.
DBSCAN: Excellent for detecting anomalies and clusters of arbitrary shape, particularly in scenarios with noise, like fraud detection.
A Comparative Analysis of Machine Learning Algorithmic
How Decision Trees Work in machine learning: Decision Trees are non-parametric, supervised learning algorithms used for both classification and regression tasks. The model splits the data into subsets based on the values of input features, forming a tree-like structure where each node represents a feature, each branch represents a decision rule, and each leaf represents the outcome