3 posts tagged with "Linear Regression"

Introduction

Gradient descent is a fundamental optimization algorithm used in machine learning to minimize the cost function and find the optimal parameters of a model. In the context of linear regression, gradient descent helps in finding the best-fitting line by iteratively updating the model parameters. This article delves into the mechanics of gradient descent in linear regression, focusing on how the parameters are updated and the impact of the sign of the gradient.

Introduction​

Linear regression is a fundamental algorithm in supervised machine learning, widely used for predicting continuous outcomes. It models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. This article delves into the components of linear regression, explaining how inputs, parameters, and the cost function work together to create a predictive model.

Introduction​

Linear regression is one of the foundational algorithms in machine learning, particularly useful for predicting continuous outcomes. In finance, it serves as a powerful tool for modeling and predicting stock returns based on various market indicators. This article delves into the application of linear regression for predicting daily returns of Amazon stock using a set of financial indices.