Pythonで学ぶ線形回帰とロジスティック回帰

import math

# Linear regression: find the line that best fits the points (least squares).
xs = [1, 2, 3, 4, 5]
ys = [2.1, 3.9, 6.2, 8.1, 9.8]
mean_x, mean_y = sum(xs) / len(xs), sum(ys) / len(ys)
slope = sum((x - mean_x) * (y - mean_y) for x, y in zip(xs, ys)) / sum((x - mean_x) ** 2 for x in xs)
intercept = mean_y - slope * mean_x
print(f"line: y = {slope:.2f}x + {intercept:.2f}")
print("predict x=6:", round(slope * 6 + intercept, 2))

# Logistic regression turns a score into a probability with the sigmoid.
def predict_pass(hours_studied, weight=1.5, bias=-4.0):
    return 1 / (1 + math.exp(-(weight * hours_studied + bias)))

for hours in (1, 3, 5):
    p = predict_pass(hours)
    print(f"{hours} hours -> pass probability {p:.2f} -> {'pass' if p >= 0.5 else 'fail'}")