# Untitled

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python
2 years ago
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```import numpy as np
import random
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold
np.random.seed(101)
random.seed(123)

hold_out_errors = []
loocv_errors = []
cv5_errors = []

for _ in range(100):
n = 100
x = np.random.uniform(-3, 3, n)
ones = np.repeat(1, n)
X = np.array([x, ones]).T
y = 2*x+1 + np.random.normal(0, 1, n)

# Hold out
indices = np.array(random.sample(range(n), int(n/2)))
reg = LinearRegression().fit(X[indices], y[indices])
preds = reg.predict(X[~indices])
error = np.mean((preds - y[~indices])**2)
hold_out_errors.append(error)

# 5-fold
kf = KFold(n_splits=5)

errors = []
for train_index, test_index in kf.split(x):
reg = LinearRegression().fit(X[train_index], y[train_index])
preds = reg.predict(X[test_index])
error = np.mean((preds - y[test_index])**2)
errors.append(error)

errors = np.array(errors)
cv5_errors.append(errors.mean())

# LOOCV
kf = KFold(n_splits=n)

errors = []
for train_index, test_index in kf.split(x):
reg = LinearRegression().fit(X[train_index], y[train_index])
preds = reg.predict(X[test_index])
error = np.mean((preds - y[test_index])**2)
errors.append(error)

errors = np.array(errors)
loocv_errors.append(errors.mean())

hold_out_errors = np.array(hold_out_errors)
cv5_errors = np.array(cv5_errors)
loocv_errors = np.array(loocv_errors)

print("Hold out")
print(hold_out_errors.mean())
print(hold_out_errors.std())

print("CV5")
print(cv5_errors.mean())
print(cv5_errors.std())
print("LOOCV")
print(loocv_errors.mean())
print(loocv_errors.std())```