![]() ![]() Discretizing y to predict the output, either win or lose, isn’t a great technique. As a result, a y (win) value flew out of the equation. You note the value x and pass it to the trained math equation described above. Later, you want to estimate the possibility of making the shot from a specific distance. As a prerequisite, you played for a month, jotted down all the values for x and y, and now you insert the values into the equation. The relation between the win (y) and distance (x) is given by a linear equation, y = mx + c. ![]() Let’s suppose you’re going to predict the answer using linear regression. On the whole, it’s about predicting whether you make the basket or not. To better understand how this process works, let’s look at an example.Ĭonsider a case where you want to sketch a relation between your basketball shot’s accuracy and the distance you shoot from. Logistic regression uses probabilities to distinguish inputs and thereby puts them into separate bags of output classes. We’ll also go over how to code a small application logistic regression using TensorFlow 2.0. There’s a lot more in the box, though, and so, in this article, we’ll explore every minute detail to understand logistic regression. With logistic regression, though, we can segregate the processed inputs into discrete classes by estimating the probabilities. Linear regression wouldn’t be able to solve this problem because the output is discrete. Consider an example in which the output juggles between true and false. Having said that, there are scenarios where classification comes into the picture. Unfortunately, only a small set of problems actually deal with continuous values. In linear regression, the output is a continuously valued label, such as the heat index in Atlanta or the price of fuel. The cost function is the element that deviates the path from linear to logistic. I n this piece, I’m going to look at logistic regression, which is just like linear regression, but with a different cost function. ![]() Recently, I discussed linear regression analysis in this space. ![]()
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January 2023
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