Logistic regression example (linear decision boundary), using simdfied

· Machine Learning, MLPlayground, simdfied

Let’s try to predict our chances of getting admitted to Msc studies, based on our Bsc degree GPA and years of experience.

Our csv should look like: 2 “X” columns: Bsc-GPA and experience and a “y” column of if-admitted value (0 / 1).

We can simulate some data with excel-like functions (I’m using LibreOffice calc on Ubuntu):

Use random values for Bsc-GPA and experience, and this formula for admittance, taking into account both “X” values:

=IF(A2<0.7, 0,

IF(A2>0.89, 1,

IF((A2+0.02*B2)>0.84, 1, 0)



For example:

bsc-gpa, #experience, admitted
0.76,  6,  1
0.81,  1,  0
0.77,  2,  0
0.82,  5,  1

Now let’s drag this csv to MLPlayground.org:


Our plot makes a visual “sense” with our admittance algorithm. We can notice that a very good GPA will get you admitted even without an experience, and a very low one won’t, regardless experience. In the middle we have all candidates with a mixture of both.

Let’s hit logistic regression:


And after some more tweaks, with a better cost and, thus, training-accuracy:


In this case, indeed a simple linear decision boundary was more sufficient for getting a decision boundary. When you think about it, it actually reflects the “linear logic” we used for our admittance formula. We can see that there aren’t any “orange” points below ~0.7 GPA, and every point is orange above ~0.83 GPA. The rest indeed reflects the linear formula of some “y=mx+b” ..

Finally, a prediction: for a candidate with a Bsc GPA of 80 and more than 4 years of experience – we can predict with high confidence an admittance 🙂


In a more real-life example, we’ll probably need a non-linear decision boundary capability, like polynomial and Gaussian kernels, which I’ll post about later.

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