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Dimension reduction

Dimension reduction is the process of reducing the number of variables (dimensions) in a dataset while keeping its most important information. It is a powerful technique for simplifying complex data, which offers benefits such as improved computational efficiency, better model performance, and easier data visualization.

Why reduce dimensions?

💡 Curse of dimensionality: When a dataset has too many dimensions relative to the number of data points, it can become sparse, making it difficult for machine learning models to find meaningful patterns.
🔑 Eliminate redundancy and noise: Datasets often contain variables that are highly correlated or irrelevant, adding noise and complexity that can confuse models.
📊 Improve visualization: The human brain is limited to visualizing data in two or three dimensions. Dimensionality reduction allows you to represent high-dimensional data in a way that is easier for people to understand.
🎯 Increase efficiency: Fewer dimensions mean less computational time and resources are needed to process the data, which is especially important for large datasets.
⚡️ Prevent overfitting: By simplifying the dataset and removing noise, a model is less likely to learn the random fluctuations in the data and more likely to generalize well to new data.

Common techniques
There are two primary approaches to dimensionality reduction:

1. Feature extraction
This method transforms the original variables into a new, smaller set of variables (components) that are combinations of the original ones.
👉 Principal Component Analysis (PCA): A popular unsupervised method that creates new, uncorrelated components, ordered by the amount of variance they explain.
👉 Factor Analysis (EFA): An unsupervised method used to identify underlying, unobserved (latent) factors that cause the correlations among the observed variables.
👉 t-SNE (t-Distributed Stochastic Neighbor Embedding): A nonlinear method especially useful for visualizing high-dimensional data by placing similar data points closer together in a lower-dimensional space.

2. Feature selection
This method selects a subset of the most relevant original variables, discarding the rest. It does not transform the variables.

Filter methods: Use statistical measures to score features and keep the best ones, for example, by filtering out low-variance or highly correlated variables.
Wrapper methods: Evaluate different subsets of features by training and testing a model with each subset to see which performs best.

https://medium.com/@souravbanerjee423/demystify-the-power-of-dimensionality-reduction-in-machine-learning-26b70b882571

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Medium

Demystify the Power of Dimensionality Reduction in Machine Learning

In the world of machine learning, navigating the vast landscape of high-dimensional data can be as thrilling as it is challenging. Imagine…

144 viewsMike, 18:57