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What is Dimensionality Reduction?

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Dimensionality Reduction is the process of reducing the number of variables or features in a dataset while retaining its essential information. By eliminating irrelevant or redundant features, businesses can simplify data analysis, improve model performance, and reduce computational complexity. Dimensionality Reduction techniques include Principal Component Analysis (PCA) and t-SNE.

Other Definitions

Feature Extraction

Feature Extraction refers to the process of identifying and selecting the most relevant features from raw data to enhance AI model performance. By extracting…

Knowledge-Based Systems

Knowledge-Based Systems are AI systems that utilise domain-specific knowledge and rules to make informed decisions or provide expert advice. These systems incorporate human expertise…

Deep Reinforcement Learning

Deep Reinforcement Learning is a subset of Machine Learning that combines Deep Learning and Reinforcement Learning. It involves training AI models to make decisions…

Human-in-the-Loop

Human-in-the-loop refers to a collaborative approach where humans and AI systems work together to achieve optimal results. It involves combining human expertise, judgement, and…
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