Jun 28, 2024 · Linear and non-linear classification using SVMs are powerful techniques for handling various classification tasks. Linear SVMs are efficient and effective for linearly separable data, while non-linear SVMs, empowered by the kernel trick, excel in handling complex, non-linear data distributions.

Instead, we use various types of non-linear projections that attempt to minimize artifacts and that strike different balances between conserving areas or angles relative to the true shape lines on the globe (Figure 3.11).

Nov 21, 2024 · Two primary types of regression models are linear regression and nonlinear regression. This article delves into the key differences between these models, their applications, and their advantages and limitations. What are Linear and Nonlinear Regression?

Mar 18, 2025 · The 3D SHAP Value Visualization captures non-linear feature interactions, with Feature 1 and Feature 2 as axes and color intensity denoting SHAP impact. Curved decision boundaries confirm...

Dec 9, 2024 · In this blog post, we have learned about t-SNE, a popular dimensionality reduction technique that can visualize high-dimensional non-linear data in a low-dimensional space. We have explained the main idea behind t-SNE, how it works, and its applications.

This module explores how non-linear transformations of our variables (namely through logarithmic transformation), as well as polynomial regression, can be employed to model non-linear relationships effectively within a linear framework.

Jan 9, 2024 · Understanding the type of relationship between variables is critical in data science, particularly for model selection. Linear regression models suit linear relationships, while nonlinear...

Apr 4, 2019 · Let’s learn how we can transform nonlinear data-sets to linear one’s using fundamentals of Mathematics and visualization of curves. Assume the following dataset is given to us and the...

Sep 13, 2023 · Firstly, one can fit a non-linear curve using linear regression by either using higher-order polynomial terms or applying a basis function (in other words, apply a log or sqrt transformation on the explanatory variables). Secondly, one may choose simplicity over complex models as linear models are easy to interpret and explain.

Feb 23, 2015 · In this overview, commonly used dimensionality reduction techniques for data visualization and their properties are reviewed. Thereby, the focus lies on an intuitive understanding of the underlying mathematical principles rather than detailed algorithmic pipelines.