Artifact of #15

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Language: python
Environment: python:3.13 with numpy 2.5.4 and scikit-learn 1.9.1; reads the Pima CSV from raw.githubusercontent.com
Command: python main.py 6
sha256: 6f8556777416ca418d425b1346fee1c4b6b729456c03c71286ea0ec6caa5e6bc

Code

# Breiman (2001) Table 2, row 'Diabetes', column 'One tree' (paper: 33.1 %; same row: Selection 24.2, Adaboost 26.6, Forest-RI single input 24.3).
# Pima Indians diabetes, 768 rows, 8 inputs, original file with zeros left as they are. Random 10% hold-out (76 cases),
# one fully grown CART tree (Gini, no pruning), repeated 100 times and averaged. One average = one seed.
import sys, urllib.request
import numpy as np
from sklearn.tree import DecisionTreeClassifier
URL = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
raw = urllib.request.urlopen(URL, timeout=60).read().decode()
rows = [r.split(",") for r in raw.strip().splitlines()]
X = np.array([[float(v) for v in r[:8]] for r in rows]); y = np.array([int(r[8]) for r in rows])
n, M = X.shape
def one_rep(rng):
    idx = rng.permutation(n); nt = n // 10
    te, tr = idx[:nt], idx[nt:]
    t = DecisionTreeClassifier(random_state=int(rng.integers(2**31 - 1))).fit(X[tr], y[tr])
    return np.mean(t.predict(X[te]) != y[te])
seeds = int(sys.argv[1]) if len(sys.argv) > 1 else 6
print("rows", n, "inputs", M, "test cases per repetition", n // 10)
res = []
for seed in range(seeds):
    rng = np.random.default_rng(seed)
    e = 100 * np.mean([one_rep(rng) for _ in range(100)])
    res.append(e); print(f"seed {seed}: mean test error over 100 repetitions = {e:.2f}%")
print(f"mean over {seeds} seeds {np.mean(res):.2f}%  sd across seeds {np.std(res, ddof=1):.2f}")

Expected output

rows 768 inputs 8 test cases per repetition 76
seed 0: mean test error over 100 repetitions = 30.12%
seed 1: mean test error over 100 repetitions = 29.95%
seed 2: mean test error over 100 repetitions = 29.59%
seed 3: mean test error over 100 repetitions = 29.71%
seed 4: mean test error over 100 repetitions = 30.04%
seed 5: mean test error over 100 repetitions = 28.63%
mean over 6 seeds 29.67%  sd across seeds 0.55

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