Logistic Regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values).
Logistic regression is like linear regression in that the goal is to find the values for the coefficients that weight each input variable.
Unlike linear regression, the prediction for the output is transformed using a non-linear function called the logistic function.
The logistic function looks like a big S and will transform any value into the range 0 to 1. This is useful because we can apply a rule to the output of the logistic function to snap values to 0 and 1 (e.g. IF less than 0.5 then output 1) and predict a class value.
Because of the way that the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0 or class 1. This can be useful on problems where you need to give more rationale for a prediction.
Like linear regression, logistic regression does work better when you remove attributes that are unrelated to the output variable as well as attributes that are very similar (correlated) to each other.
It’s a fast model to learn and effective on binary classification problems.
logistic-regression-example
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the decision boundary for logistic regression is linear. the activation function is logistic, with a typical classification discrimination level of 0.5.