Search results
Sep 10, 2024 · Principal Component Analysis (PCA) is used to reduce the dimensionality of a data set by finding a new set of variables, smaller than the original set of variables, retaining most of the sample’s information, and useful for the regression and classification of data.
Principal Component Analysis is an unsupervised learning algorithm that is used for the dimensionality reduction in machine learning. It is a statistical process that converts the observations of correlated features into a set of linearly uncorrelated features with the help of orthogonal transformation.
Table of Contents. 1. What is PCA? 2. When to use the Principal Component Method of Factor Analysis? 3. Principal Component Analysis example. 4. Why Principal Component Analysis is useful? 5. Step by step explanation of Principal Component Analysis. 5.1. STEP 1: STANDARDIZATION. 5.2. STEP 2: COVARIANCE MATRIX COMPUTATION. 5.3.
Mar 28, 2024 · You probably used scikit-learn’s PCA module in your model trainings or visualizations, but have you wondered about the mathematical meaning and theory behind it? In this tutorial I will...
Nov 6, 2024 · What is Principal Component Analysis (PCA) in Machine Learning? Principal Component Analysis can be abbreviated PCA; PCA comes under the Unsupervised Machine Learning category; The main goal of PCA is to reduce the number of variables in a data collection while retaining as much information as feasible.
In this tutorial, I will first implement PCA with scikit-learn, then, I will discuss the step-by-step implementation with code and the complete concept behind the PCA algorithm in an easy to understand manner. Principal Components Analysis (PCA) – Better Explained. Photo by RockyClub.
Jun 8, 2024 · In this article, I show the intuition of the inner workings of the PCA algorithm, covering key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues, then we’ll implement a Python class to encapsulate these concepts and perform PCA analysis on a dataset.
