iframe-proxy | Sunbelt Computer Software

This repository was archived by the owner on May 12, 2026. It is now read-only.

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from sklearn import tree
TEXTURE_BUMPY = 0
TEXTURE_SMOOTH = 1
LABEL_APPLE = 0
LABEL_ORANGE = 1
    LABEL_APPLE: 'apple',
    LABEL_ORANGE: 'orange',
def classify(to_classify):
    features = [
        [90, TEXTURE_BUMPY],
        [140, TEXTURE_SMOOTH],
        [130, TEXTURE_SMOOTH],
        [150, TEXTURE_BUMPY],
        [170, TEXTURE_BUMPY],
    labels = [
        LABEL_ORANGE,
        LABEL_APPLE,
        LABEL_APPLE,
        LABEL_ORANGE,
        LABEL_ORANGE
    clf = tree.DecisionTreeClassifier()
    clf = clf.fit(features, labels)
    return clf.predict(to_classify)
def main():
    to_classify = [
        [160, TEXTURE_BUMPY],
        [145, TEXTURE_BUMPY],
        [120, TEXTURE_SMOOTH],
        [100, TEXTURE_BUMPY],
    decisions = classify(to_classify)
    for decision in decisions:
        print("It's a(n):", LABELS[decision])
if __name__ == '__main__':

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