Grab the slides: slides.com/cheukting_ho/legend-data-log-reg
Every Monday 5pm UK time
by Cheuk Ting Ho
When what you want to predict has only 2 outcomes. For example,
If we find the right t-asix the data will look like a Sigmoid function then we can distingulish 0 and 1
We find the right (set of) b by mininising the error (the slider game)
Remember how we measure the error of the linear regrestion last time?
Similar to the sum of error square, the standard way of measure how wrong (cost function) of the model form the actually training data is root mean square error (RMSE)
there for the cost function of linear regression is:
Y'' = 1/ 1+ e**-(b1X1+b0)
where if Y'' > 0.5, Y' =1; else, Y'=0
🤔
We want this minimized!!!
Every Monday 5pm UK time
Get the notebooks: https://github.com/Cheukting/legend_data