# AI News, Machine Learning FAQ ## Machine Learning FAQ

Index The short answer is: Logistic regression is considered a generalized linear model because the outcome always depends on the sum of the inputs and parameters.

Although logistic regression produces a linear decision surface (see the classification example in the figure below) this logistic (activation) function doesn’t look very linear at all, right!?doesn’t look very linear at all, right!?

Let’s assume we have a sample training point x consisting of 4 features (e.g., sepal length, sepal width, petal length, and petal width in the Iris dataset): Now, let’s assume our weight vector looks like this: Let’s compute z now!

= wTx = 10.5 + 20.5 + 30.5 + 40.5 = 5 Not that it is important, but we have a 99.3% chance that this sample belongs to class 1: Φ(z=148.41) = 1 / (1 + e-5) = 0.993 The key is that our model is additive our

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This video is part of the Udacity course "Deep Learning". Watch the full course at

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❤︎² Logarithms... How? (mathbff)

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