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Publisher ‏ : ‎ Scientific Books (January 16, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 231 pages
ISBN-10 ‏ : ‎ 1716005132
ISBN-13 ‏ : ‎ 978-1716005138
Item Weight ‏ : ‎ 1.54 pounds
Dimensions ‏ : ‎ 8.27 x 0.53 x 11.69 inches

Machine learning is a rapidly growing field that involves training algorithms to make predictions or decisions based on data. One popular type of machine learning is supervised learning, where the algorithm is trained on a labeled dataset to make predictions on new, unseen data. One common technique in supervised learning is regression, where the goal is to predict a continuous value based on input features.

SAS and MATLAB are two popular tools used for implementing machine learning algorithms. Let’s take a look at how regression can be implemented using these tools:

1. SAS:
   SAS is a powerful statistical software that provides a wide range of tools for data analysis and machine learning. In SAS, you can easily implement regression models using the PROC REG or PROC GLM procedures. Here’s a simple example of how to perform linear regression in SAS:

   
   data mydata;<br />
   input x y;<br />
   datalines;<br />
   1 2<br />
   2 4<br />
   3 6<br />
   4 8<br />
   ;<br />
   run;<br />
   <br />
   proc reg data=mydata;<br />
   model y = x;<br />
   run;<br />
   ```<br />
   <br />
   In this example, we have a simple dataset with two columns, x and y. We use the PROC REG procedure to fit a linear regression model to predict y based on x.<br />
   <br />

2. MATLAB:
   MATLAB is a popular programming language and environment for numerical computing. In MATLAB, you can use the fitlm function to perform linear regression. Here’s an example of how to implement linear regression in MATLAB:

   
   x = [1; 2; 3; 4];<br />
   y = [2; 4; 6; 8];<br />
   <br />
   mdl = fitlm(x, y);<br />
   disp(mdl);<br />
   ```<br />
   <br />
   In this example, we create vectors x and y with our input and output variables, respectively. We then use the fitlm function to fit a linear regression model to predict y based on x.<br />
   <br />
   Overall, regression is a powerful technique in supervised learning that can be easily implemented using tools like SAS and MATLAB. By understanding and applying regression techniques, you can make accurate predictions and decisions based on your data.

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