I am currently using sklearn
scale to preprocess my X data before being put into a perceptron -
mean/stddev so as to prevent the data converging to infinity or 0. My perceptron returns the weights + bias after the network has been trained:
X = preprocessing.scale(X)
After processing the X and Y data through my perceptron I am returned with weights. From these weights I can calculate the line of best fit:
ls = cp.linspace(cp.min(X), cp.max(X)) best_fit = w+w*ls
w is a bias. This
best_fit line is accurate but it is relative to the preprocessed X rather than the original X which I would like to plot. What is the technique to make these weights relative to the original X values if it is possible?