FAQ: Logistic Regression - Logistic Regression

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This community-built FAQ covers the “Logistic Regression” exercise from the lesson “Logistic Regression”.

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Machine Learning

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[careershifter]

EDIT: I found the answer to my own inquiry below in slide 9/11 of this lesson. Basically it does exactly what i described in my last paragraph. This lesson doesn’t show to us explicitly the gradient descent being performed on the log loss because the concept is already covered in the Linear Regression Lesson.

To predict the probability of a data sample belonging to a class, we:

1. initialize all feature coefficients and intercept to 0
2. multiply each of the feature coefficients by their respective feature value to get what is known as the log-odds
3. place the log-odds into the sigmoid function to link the output to the range [0,1] , giving us a probability

If the feature coefficient is 0 and you multiply that to the feature value, wouldn’t that just give you 0? Which was in fact used in a later part of the lesson to show the worst possible log-loss?

Or are we supposed to start with the worst possible log loss, and then iterate again this time with new adjusted coefficients resulting in a log loss that is smaller and again and again until the log loss is minimized, thereby finally giving us the best coefficients for minimal log loss?

[anuarsnchez662072698]

In the context of this excercise, what are the corect values of the question: " What is the lowest possible probability that can be predicted, and what is the highest possible probability that can be predicted? Enter your answer in the variables lowest and highest , respectively."