Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of features or variables in a dataset while retaining as much information as possible. This is done by transforming the data from a high-dimensional space to a lower-dimensional space, where each dimension represents a combination of the original features. The goal of dimensionality reduction is to simplify the data and make it easier to analyze, visualize, and model. It can also help to reduce noise and improve the performance of machine learning algorithms by reducing the risk of overfitting. There are two main types of dimensionality reduction techniques: feature selection, which selects a subset of the original features, and feature extraction, which creates new features that are combinations of the original features. Some common dimensionality reduction techniques include Principal Component Analysis (PCA), t-SNE, and Autoencoders.
Your Previous Searches
Random Picks
- Computer Programs: Computer programs are sets of instructions that tell a computer what to do. In the context of data science and artificial intelligence, computer programs are used to process and analyze large amounts of data, build predictive models, and au ... Read More >>
- Plug-and-play: Plug-and-play refers to the ability of a system or device to work immediately after being connected to a computer or network, without the need for additional configuration or installation of drivers. In the context of data science and artif ... Read More >>
- Outcomes: Outcomes refer to the results or consequences of a particular action or decision. In the context of data science, outcomes are the desired results that a company or organization hopes to achieve through the use of data analysis and machine ... Read More >>
Top News
Tech giants see emissions surge 150 percent in 3 years amid AI boom: UN...
Artificial intelligence, cloud computing and data centres led to a spike in electricity demand between 2020 and 2023....
News Source: Al Jazeera English on 2025-06-06
‘Ghost networks' are harming patients, but attempts to eliminate them have fal...
Insurance companies often refer patients to lists of providers who are unreachable, out of network or don’t accept new patients....
News Source: NBC News on 2025-06-05
Palantir CEO Karp says AI is dangerous and 'either we win or China will win'...
Palantir CEO Alex Karp said the artificial intelligence arms race between the U.S. and China will culminate in one country coming out on top....
News Source: NBC News on 2025-06-05
Palantir has soared 74% this year alone. 3 reasons why it's been one of the worl...
Palantir was the second-most bought stock among retail traders in the last five days, according to a firm that tracks flows from individual investors....
News Source: Business Insider on 2025-06-05
Harris-Walz campaign may have been targeted by iPhone hackers, cybersecurity fir...
One of the few companies to specialize in iPhone cybersecurity said that it has uncovered evidence of a potentially groundbreaking hacking campaign....
News Source: NBC News on 2025-06-05