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PLAGIARISM SCAN REPORT

Words 231 Date December 12,2018

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78% Plagiarism

22% Unique

7 Plagiarized Sentences

2 Unique Sentences

Content Checked For Plagiarism Logistic regression is named for the function used at the core of the method, the logistic function. The logistic function, also called the sigmoid function was developed by statisticians to describe properties of population growth in ecology, rising quickly and maxing out at the carrying capacity of the environment. It’s an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1, but never exactly at those limits. Logistic regression uses an equation as the representation, very much like linear regression. Input values (x) are combined linearly using weights or coefficient values (referred to as the Greek capital letter Beta) to predict an output value (y). A key difference from linear regression is that the output value being modeled is a binary values (0 or 1) rather than a numeric value. Below is an example logistic regression equation: y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x)) Where y is the predicted output, b0 is the bias or intercept term and b1 is the coefficient for the single input value (x). Each column in your input data has an associated b coefficient (a constant real value) that must be learned from your training data. The actual representation of the model that you would store in memory or in a file are the coefficients in the equation (the beta value or b’s).

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Logistic Regression Mathematics Explained – Artificial Inteligence...Compare text logistic regression is named for the function used at the core of the method, the logistic function. the logistic function, also called the sigmoid function was developed by statisticians to describe properties of population growth in ecology, rising quickly and maxing out at the carrying... https://medium.com/artificial-inteligence-and-cryptocurrecncy/logistic-regression-mathematics-explained-55d8bc 73afa7

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