PCA in machine learning - Learn Data Science with Travis

PCA-in-machine-learning

Principal Component Analysis (PCA) is a powerful technique in machine learning used for dimensionality reduction. It transforms a dataset with many features into a smaller set of uncorrelated variables called principal components, while retaining most of the original variance. This process helps to simplify models, reduce computational costs, and mitigate the curse of dimensionality, which can negatively impact the performance of machine learning algorithms. PCA is widely applied in various fields, including image processing, data visualization, and feature extraction, making it an essential tool for data scientists and machine learning practitioners.

Think twice before you use Principal Component Analysis in supervised learning tasks

Towards Data Science

Principal Component Analysis (PCA) is one of the most popular machine learning technique. It reduces the dimension of a given data set, making the data set more approachable and computationally…

PCA Explained with DPlotly Visualizations

Towards Data Science

PCA (Principal component analysis) is an unsupervised learning algorithm that finds the relations among features within a dataset. It is also widely used as a preprocessing step for supervised…

Implementing PCA in Python with sklearn

Analytics Vidhya

Principal Component Analysis (PCA) is a commonly used dimensionality reduction technique for data sets with a large number of variables. Since many machine learning algorithms suffer from the curse…

PCA — Machine Learning Algorithms with Implementation in Python

Python in Plain English

Principal Component Analysis, a Machine Learning, Artificial Intelligence, and Data Science algorithm, and how to implement it in code using Python (Scikit-Learn)

PCA — Demystified.

Analytics Vidhya

Often in machine learning, the datasets have many features with which the predictions are to be made. Principal Component Analysis (PCA) is a technique employed to reduce the dimensions. It is often…

A Complete Guide to Principal Component Analysis — PCA in Machine Learning

Towards Data Science

Principal Component Analysis or PCA is a widely used technique for dimensionality reduction of the large data set. Reducing the number of components or features costs some accuracy and on the other…

Applying PCA in R

Analytics Vidhya

PCA is a powerful Machine Learning technique which can be useful for multiple tasks : data visualization, data analysis and exploration, reducing variance in datasets, increase the Signal-To-Noise…

Principal Component Analysis explained

Towards Data Science

Principal Components Analysis (PCA) is one of the most famous algorithms in Machine Learning (ML), it aims to reduce the dimensionality of your data or to perform unsupervised clustering. PCA is…

Principal Component Analysis (PCA) with Scikit-learn

Towards Data Science

Hi everyone! This is the second unsupervised machine learning algorithm that I’m discussing here. This time, the topic is Principal Component Analysis (PCA). At the very beginning of the tutorial…

Dimensionality Reduction — Can PCA improve the performance of a classification model?

Towards Data Science

Principal Component Analysis (PCA) is a common feature extraction technique in data science that employs matrix factorization to reduce the dimensionality of data into lower space. In real-world…

Principal Components of PCA

Towards Data Science

Principal Component Analysis (PCA) is used in machine learning applications to reduce the dimensionality of the data. It has been especially useful for image compression among other applications. In…

How Where and When we should use PCA

Towards Data Science

Perhaps the most popular dimensionality reduction technique in machine learning is Principal Component Analysis. Well, practically everything has already been written about the PCA. However, I have…