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<p><strong>WHAT IS LOGISTIC REGRESSION? </strong></p> <p>Logistic regression is a technique in <strong><a href="https://www.google.com/maps/place/ExcelR+Solutions+-+Data+Science+Training+Course+%7C+Digital+Marketing+Course+in+Pune/@18.5584617,73.7889225,17z/data=!3m1!4b1!4m5!3m4!1s0x3bc2bf37817aae43:0x6c49e2eda8b01c77!8m2!3d18.5584566!4d73.7911112">data science technology</a></strong> which is used for predictions and classifications. We use logistic regression when we want the result or output in the form of yes or no. This means either that condition will occur or not occur. For this, the data has to be divided into two parts: The first part for the conditions which will occur and the second part for the conditions which will not occur. Logistic regression is like probability. In probability too, we find outcomes in the form of yes or no. There yes or no is denoted by 0 and 1. This condition will be clearer if we cite an example. Suppose we have to detect an email whether it is spam or not spam. In this condition, all the emails will be divided into two parts, one will be non-spam and the other will be spam. This is what we call logistic regression.</p> <p><strong>WHAT IS THE NEED OF LOGISTIC REGRESSION? </strong></p> <p>Many people will be thinking that this task can be done with the help of linear regression too. What is the need for logistic regression? Linear regression works on the unbounded data. Linear regression is not suitable if we want classifications in our data. Here, we want a technique which will give results between 0 and 1 and either 0 or 1. That is why it is difficult to implement linear regression here and even if we implement it, we do not get the accurate results.</p> <p><strong>TYPES OF LOGISTIC REGRESSION</strong></p> <p>Here are some different types of logistic regression--></p> <ul> <li>Binary logistic regression</li> <li>Multinomial logistic regression</li> <li>Ordinal logistic regression</li> </ul> <p><strong>WHAT IS BINARY LOGISTIC REGRESSION?</strong></p> <p>In binary logistic regression, there will be only two conditions, yes or no. Above we have taken an example of email spam detection; either the email will be spam or not spam. Such conditions come under <strong><a href="https://www.excelr.com/data-science-course-training-in-pune/">binary logistic regression</a></strong>.</p> <p><strong>WHAT IS MULTINOMIAL REGRESSION?</strong></p> <p>In multinomial regression, the data is divided into more than two parts. Mostly, this regression is used for classifying the data in three types. For example, we can categorize the food into three types, non-veg, veg and vegan. This type of classification comes under multinomial regression.</p> <p><strong>WHAT IS ORDINAL REGRESSION? </strong></p> <p>The ordinal regression is used when we want to divide our data into more than three parts.</p> <p><strong>WHAT IS DECISION BOUNDARY? </strong></p> <p>Decision boundary decides the range of a class. This decides which data will belong to which class. For this, a threshold value is set. For example, we have two classes of email in our example; a threshold value will be set for these classes. Let us say the threshold value is 0.8. If an email has the value ≤ 0.8, then the email will be non-spam and if it is greater, then the email will be spam.</p> <p><strong>CONCLUSION</strong></p> <p>Data science technology has a growing importance in the market. This shows that this technology is a very nice career option. Those people who are interested in acquiring more knowledge about the <a href="https://www.fbioyf.unr.edu.ar/evirtual/blog/index.php?userid=62112">data science course in Pune</a> can visit the link and enroll themselves here.</p> <p> </p>
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