🥊 Regression vs Autoregression 🥊

In Machine Learning 🤖, both are common techniques used for modelling relationships between variables. 📉

But, have you ever wondered about the difference? 🤔

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In Machine Learning, both regression and autoregression are common techniques used for modelling relationships between variables.

But what's the difference between the two? 🤔

Let's break it down 👇

1️⃣ Regression is a statistical technique for modelling the relationship between a dependent variable (also known as response or output) and one or more independent variables (also known as predictors or inputs).

Regression aims to estimate the parameters' values in a mathematical model that best fits the observed data.

It's a widely used technique in many fields and can be used to make predictions about future observations.

2️⃣ Autoregression is a type of regression model where the output variable is predicted based on its own previous values.

In other words, it's a regression of the variable onto itself.

Autoregression is commonly used in time series analysis, where the goal is to model the dependencies between the values of a variable over time.

It's a powerful tool for forecasting future values based on past observations.

▶️ TL;DR

Regression is a general technique for modelling the relationship between variables, while autoregression is a specific type of regression model used for time series analysis.

So next time you're choosing between regression and autoregression, consider the type of data you're working with and the goal of your analysis.

And remember, both techniques can be incredibly useful!

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