def r_squared(alpha, beta, x, y):
"""the fraction of variation in y captured by the model, which equals
1 - the fraction of variation in y not captured by the model"""
if (1.0 - (sum_of_sqerrors(alpha, beta, x, y) /
total_sum_of_squares(y))) >= 0.75:
print("1등급 R^2")
elif (
1.0 -
(sum_of_sqerrors(alpha, beta, x, y) / total_sum_of_squares(y))) >= 0.5:
print("2등급 R^2")
elif (1.0 - (sum_of_sqerrors(alpha, beta, x, y) /
total_sum_of_squares(y))) >= 0.25:
print("3등급 R^2")
else:
print("4등급 R^2")
return 1.0 - (sum_of_sqerrors(alpha, beta, x, y) / total_sum_of_squares(y))
def multiple_r_squared(xs: List[Vector], ys: Vector, beta: Vector) -> float:
sum_of_squared_errors = sum(error(x, y, beta)**2 for x, y in zip(xs, ys))
return 1.0 - sum_of_squared_errors / total_sum_of_squares(ys)