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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

Forecasting

Forecasting involves predicting future outcomes or trends based on historical data and patterns. By analysing past data and applying statistical techniques, businesses can make…

Decision Trees

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Bayesian Networks

Bayesian networks are Probabilistic Graphical Models that represent and evaluate uncertainty and conditional dependencies between variables. Industries such as healthcare and finance use Bayesian…

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…
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