Linear Regression Algorithm in Machine Learning through MATLAB

Abstract

In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error

Insights into the Effects of Co-Regulated Factors on Li1.3Al0.3Ti1.7(PO4)3 Solid Electrolyte Preparation: Sources, Calcination Temperatures, and Sintering Temperatures

“…Linear regression is also a fundamental model in machine learning (supervised learning) that establishes the relationship between the dependent and independent variables and predicts new values based on existing data. The expression for this model is also known as the hypothesis equation, and is given below: Y = a + b X 1 + c X 2 + d X 3 + ··· where Y is the response (dependent variable), a is the intercept, and X1, X2, and X3 and b , c , and d are the independent variables and their respective slopes. In this work, the relationship between the total ionic conductivity ( Y ) of the LATP pellets and the source, calcination temperature, and sintering temperature (X1, X2, and X3) can be expressed by the following linear equation: Y = a + b X1 + c X2 + d X3.…”

“…Linear regression is also a fundamental model in machine learning (supervised learning) that establishes the relationship between the dependent and independent variables and predicts new values based on existing data. The expression for this model is also known as the hypothesis equation, and is given below: 44 where Y is the response (dependent variable), a is the intercept, and X1, X2, and X3 and b, c, and d Here, a linear regression analysis was performed by MATLAB, and the algorithm and code are shown in the appendix of the Supporting Information. 45−48 Table 3 shows the output coefficients of the linear regression model and the corresponding statistics.…”

“…Linear regression is also a fundamental model in machine learning (supervised learning) that establishes the relationship between the dependent and independent variables and predicts new values based on existing data. The expression for this model is also known as the hypothesis equation, and is given below: 44 Y a b c d…”

The empirical research of teamwork competency factors and prediction on academic achievement using machine learning for students in Thailand

“…In addition, teamwork competencies were used to predict the effects of academic achievement [36; 37], prediction using machine learning as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data [38]. The aspect type of machine learning is supervised learning consisted of regression by linear regression and classification by decision trees [39]. The research conceptual framework is shown in figure 1.…”

“…Prediction using machine learning as supervised learning consisted of regression by linear regression and classification by decision trees [39] as follow:…”