Apr 5, 2025 · Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It provides valuable insights for prediction and data analysis. This article will explore its types, assumptions, implementation, advantages and evaluation metrics.

In this section, we introduced traditional linear regression, where the parameters of a linear function are chosen to minimize squared loss on the training set. We also motivated this choice of objective both via some practical considerations and through an interpretation of linear regression as maximimum likelihood estimation under an ...

This chapter will focus narrowly on linear regression and the next one will extend our modeling repertoire by developing linear neural networks for classification.

In this section, we will implement the entire method from scratch, including (i) the model; (ii) the loss function; (iii) a minibatch stochastic gradient descent optimizer; and (iv) the training function that stitches all of these pieces together.

Apr 26, 2021 · With the aim of aiding beginners in understanding the relationships among different Regression algorithms, this paper characterizes a broad and thoughtful selection of recent regression algorithms, providing an organized and comprehensive overview of existing work and models utilized frequently.

Nov 18, 2023 · In the ever-evolving landscape of machine learning, linear and logistic regression stand as fundamental pillars, providing invaluable tools for understanding and predicting patterns in...

Summary <p>Deep learning uses data inputs to guess the nonlinear plane that will most correctly go through the middle of a set of data points in a more sophisticated manner than linear regression. This chapter discusses variables and how to work with them to create a linear regression. Regression boasts a long history in different domains: statistics, economics, psychology, social sciences ...

Dating back to the dawn of the 19th century, linear regression flows from a few simple assumptions. First, we assume that the relationship between the features x and targets y is linear, i.e., that y can be expressed as a weighted sum of the …

4 days ago · The linear regression (LR) method offers the advantage that optimal parameters can be calculated relatively easily, although its representation capability is limited than that of the deep learning technique. To improve deep reinforcement learning, the Least Squares Deep Q Network (LS-DQN) method was proposed by Levine et al., which combines ...

As a refresher, we will start by learning how to implement linear regression. The main idea is to get familiar with objective functions, computing their gradients and optimizing the objectives over a set of parameters. These basic tools will form the basis for more sophisticated algorithms later.