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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.
- 14 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
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 he/she st...
This is 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: $$y = b_0 + b_1x_1 + b_2x_2 + b_3x_3$$ Imagine yo...
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 model. Here, the degre...
To avoid overfitting, we use ridge and lasso regression in 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 ...
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).- Linear Regression. Regression Evaluation Metrics. Here are three common evaluation metrics for regression problems: Mean Absolute Error (MAE) is the mean of the absolute value of the errors
- Robust Regression. Robust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric methods.
- Ridge Regression. Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of coefficients. The ridge coefficients minimize a penalized residual sum of squares,
- LASSO Regression. LASSO Regression is a linear model that estimates sparse coefficients. Mathematically, it consists of a linear model trained with L1 prior as a regularizer.
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 different techniques to prepare and train a linear regression model, such as Ordinary Least Squares and Gradient Descent.
Aug 30, 2023 · Regression in machine learning is a technique used to capture the relationships between independent and dependent variables, with the main purpose of predicting an outcome. It involves training a set of algorithms to reveal patterns that characterize the distribution of each data point.
- George Lawton
Feb 1, 2022 · Learn about regression, a type of supervised learning where we predict a numeric value using a model. Explore linear, polynomial, regularized, and support vector regression models with examples and equations.
Learn how to use regression analysis, a statistical technique, to build accurate prediction models in machine learning. Explore the basics, data preparation, evaluation, and types of regression algorithms.
