Discussion of article "Data Science and Machine Learning (Part 02): Logistic Regression"
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New article Data Science and Machine Learning (Part 02): Logistic Regression has been published:
Data Classification is a crucial thing for an algo trader and a programmer. In this article, we are going to focus on one of classification logistic algorithms that can probability help us identify the Yes's or No's, the Ups and Downs, Buys and Sells.
The Linear model is passed to a logistic function (sigmoid/p) =1/1+e^t where t is the linear model the result which is values between 0 and 1. This represents the probability of a data point belonging to a class.
Instead of using y of a linear model as a dependent, its function is shown as " p" is used as dependent
p = 1/1+e^-(c+m1x1+m2x2+....+mnxn) ,case of multiple values
As said earlier, the sigmoid curve aims to convert infinity values into binary format output(0 or 1). But what if I have a data point located at 0.8, how can one decide that the value is zero or one? This is where the threshold values come into play.
The threshold indicates the probability of either winning or losing, it is located at 0.5 (center of 0 and 1).
Any value greater than or equal to 0.5 will be rounded to one, hence regarded as a winner, whilst any value below 0.5 will be rounded to 0 hence regarded as a loser at this point, it is time that we see the difference between linear and logistic regression.
Author: Omega J Msigwa