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Feb 26, 2024 · Regression, a statistical approach, dissects the relationship between dependent and independent variables, enabling predictions through various regression models. The article delves into regression in machine learning, elucidating models, terminologies, types, and practical applications.
- Linear Regression
Linear regression is a type of supervised machine learning...
- Linear Regression
Sep 2, 2024 · Linear regression is a type of supervised machine learning algorithm that computes the linear relationship between the dependent variable and one or more independent features by fitting a linear equation to observed data.
- 16 min
- Evaluating A Machine Learning Regression Algorithm
- Linear Regression in Machine Learning
- Simple Linear Regression in Machine Learning
- Multiple Linear Regression in Machine Learning
- Multivariate Linear Regression in Machine Learning
- Polynomial Regression in Machine Learning
- Ridge and Lasso Regression in Machine Learning
- Summary of Machine Learning Regression
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Let’s say you’ve developed an algorithm which predicts next week’s temperature. The temperature to be predicted depends on different properties such as humidity, atmospheric pressure, air temperature and wind speed. But how accurate are your predictions? How good is your algorithm? To evaluate your predictions, there are two important metrics to be...
Linear regressionfinds the linear relationship between the dependent variable and one or more independent variables using a best-fit straight line. Generally, a linear model makes a prediction by simply computing a weighted sum of the input features, plus a constant called the bias term (also called the intercept term). In this technique, the depen...
Simple linear regression is one of the simplest (hence the name) yet powerful regression techniques. It has one input ($x$) and one output variable ($y$) and helps us predict the output from trained samples by fitting a straight line between those variables. For example, we can predict the grade of a student based upon the number of hours they stud...
Multiple linear regressionis similar to simple linear regression, but there is more than one independent variable. Every value of the independent variable x is associated with a value of the dependent variable y. As it’s a multi-dimensional representation, the best-fit line is a plane. Mathematically, it’s expressed by: $$y = b_0 + b_1x_1 + b_2x_2 ...
As the name implies, multivariate linear regression deals with multiple output variables. For example, if a doctor needs to assess a patient’s health using collected blood samples, the diagnosis includes predicting more than one value, like blood pressure, sugar level and cholesterol level. Related Reading: What is Linear Algebra? Basic Linear Alge...
While the linear regression model is able to understand patterns for a given dataset by fitting in a simple linear equation, it might not might not be accurate when dealing with complex data. In those instances we need to come up with curves which adjust with the data rather than the lines. One approach is to use a polynomial regressionmodel. Here,...
To avoid overfitting, we use ridge and lasso regressionin the presence of a large number of features. These are the regularization techniques used in the regression field. They work by penalizing the magnitude of coefficients of features along with minimizing the error between the predicted and actual observations. Coefficients evidently increase t...
Regression is a supervised machine learning technique which is used to predict continuous values.The ultimate goal of the regression algorithm is to plot a best-fit line or a curve between the data.The three main metrics that are used for evaluating the trained regression model are variance, bias and error. If the variance is high, it leads to overfitting and when the bias is high, it leads t...Based on the number of input features and output labels, regression is classified as linear (one input and one output), multiple (many inputs and one output) and multivariate (many outputs).Learn what regression is, how it works, and how to evaluate its performance in machine learning. Explore different types of regression models, such as linear, logistic, and polynomial, with examples and code.
Learn how to use regression analysis to model the relationship between a dependent and independent variable in machine learning. Explore different types of regression, such as linear, logistic, polynomial, and support vector regression, with examples and applications.
- Linear Regression: Linear regression is used when the relationship between the dependent variable and the independent variables is assumed to be linear.
- Multiple Linear Regression: Similar to linear regression, but it involves multiple independent variables. It is used when the response variable depends on more than one predictor variable.
- Polynomial Regression: Polynomial regression is used when the relationship between the dependent and independent variables can be better approximated by a polynomial function rather than a straight line.
- Ridge Regression (L2 Regularization): Ridge regression is used to handle multicollinearity (high correlation between predictors) in multiple linear regression.
Dec 6, 2023 · Learn the basics of linear regression, a statistical and machine learning algorithm for modeling the relationship between input and output variables. Explore the representation, learning methods, data preparation and applications of linear regression.
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Oct 15, 2023 · Regression is a type of supervised learning technique in machine learning that involves predicting a continuous outcome variable based on one or more input features. In other words, the goal of regression is to build a model that can estimate the value of a target variable based on input variables. Types of Regression.
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