In this case, the dataset comprises two distinct features: memory (capacity) and cost. Let’s consider a dataset that covers RAM sizes and their corresponding costs. See More: What Is Super Artificial Intelligence (AI)? Definition, Threats, and Trends Linear Regression Equation The above features highlight why linear regression is a popular model to solve real-life machine learning problems. All these factors make such compute-intensive models expensive and unsuitable for real-time applications. The model can be trained and retrained with each new example to generate predictions in real-time, unlike the neural networks or support vector machines that are computationally heavy and require plenty of computing resources and substantial waiting time to retrain on a new dataset. The ease of computation of these algorithms allows them to be used in online settings. For example, the model can scale well regarding increased data volume (big data). Linear regression is not computationally heavy and, therefore, fits well in cases where scaling is essential. As a result, this algorithm stands ahead of black-box models that fall short in justifying which input variable causes the output variable to change. Unlike other deep learning models (neural networks), linear regression is relatively straightforward. The linear regression model is computationally simple to implement as it does not demand a lot of engineering overheads, neither before the model launch nor during its maintenance. Linear regression is a popular statistical tool used in data science, thanks to the several benefits it offers, such as: Hence, it is called the ‘best fit line.’ The goal of the linear regression algorithm is to find this best fit line seen in the above figure. Here, a line is plotted for the given data points that suitably fit all the issues. Line of regression = Best fit line for a model This analysis method is advantageous when at least two variables are available in the data, as observed in stock market forecasting, portfolio management, scientific analysis, etc.Ī sloped straight line represents the linear regression model.īest Fit Line for a Linear Regression Model Thus, linear regression is a supervised learning algorithm that simulates a mathematical relationship between variables and makes predictions for continuous or numeric variables such as sales, salary, age, product price, etc. The regression model predicts the value of the dependent variable, which is the response or outcome variable being analyzed or studied. However, the dependent variable changes with fluctuations in the independent variable. The independent variable is also the predictor or explanatory variable that remains unchanged due to the change in other variables. It is a statistical method used in data science and machine learning for predictive analysis. Linear regression is an algorithm that provides a linear relationship between an independent variable and a dependent variable to predict the outcome of future events.
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