### Programming R Squared

https://pythonprogramming.net/how-to-program-r-squared-machine-learning-tutorial/

Straight forward tutorial – plugging in the R^2 calculation into a function.

# Import Libs from statistics import mean import numpy as np import matplotlib.pyplot as plt # To set charts to save as images we need to change the default behaviour from matplotlib import style # inport style to change default behaviour of plot style.use('ggplot') # use ggplot # Define values xs = np.array([1,2,3,4,5], dtype=np.float64) # dtype lets you set the data type. Not needed for this example but useful in future ys = np.array([5,4,6,5,6], dtype=np.float64) # Define best fit function def best_fit_slope_and_intercept(xs, ys): # defining function to calculate slope (m) - passing values of xs and ys m = ( ((mean(xs)*mean(ys)) - mean(xs * ys)) / # bracket space at the start and space slash at the end allows for a carridge return in the code ((mean(xs)**2)-mean(xs**2))) ## **2 raises to the power of 2 b = mean(ys) - m*mean(xs) return m, b m, b = best_fit_slope_and_intercept(xs,ys) # Define function to square error def squared_error(ys_orig,ys_line): return sum((ys_line - ys_orig) * (ys_line - ys_orig)) # return used with calc rather than seperately first def coefficient_of_determination(ys_orig,ys_line): y_mean_line = [mean(ys_orig) for y in ys_orig] # one line for loop squared_error_regr = squared_error(ys_orig, ys_line) squared_error_y_mean = squared_error(ys_orig, y_mean_line) return 1 - (squared_error_regr/squared_error_y_mean) m, b = best_fit_slope_and_intercept(xs,ys) regression_line = [(m*x)+b for x in xs] r_squared = coefficient_of_determination(ys,regression_line) print(r_squared) #plt.scatter(xs,ys) #plt.savefig('ML_Tutorial8.png', bbox_inches='tight') #Sets the output to save an image #plt.show() # exports the image