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Dimensionality reduction is a technique that simplifies complex data by reducing the number of variables (dimensions) while preserving as much meaningful information as possible. It helps make high-dimensional data easier to visualise, analyse, and process.
High-dimensional data slows down models and can cause overfitting. Dimensionality reduction makes data more manageable and often improves model performance.
Spotify uses dimensionality reduction to compress complex audio features of songs into a simpler representation, making it faster to find similar-sounding tracks.
Clustering
Clustering is an unsupervised learning technique that groups similar data points together without predefined labels. The algorithm discovers natural patterns and structures in the data based on similarity measures.
Feature
In machine learning, a feature is an individual measurable property or characteristic of the data being used to make predictions. Features are the input variables that a model uses to learn patterns and produce outputs.
Unsupervised Learning
Unsupervised learning is a type of machine learning where the model is trained on data without labelled answers. The model discovers hidden patterns, groupings, and structures in the data on its own, without being told what to look for.
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