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February 21, 2021 19:08
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Test model can overfit
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@pytest.fixture | |
def dummy_feats_and_labels(): | |
feats = np.array([[0.7057, -5.4981, 8.3368, -2.8715], | |
[2.4391, 6.4417, -0.80743, -0.69139], | |
[-0.2062, 9.2207, -3.7044, -6.8103], | |
[4.2586, 11.2962, -4.0943, -4.3457], | |
[-2.343, 12.9516, 3.3285, -5.9426], | |
[-2.0545, -10.8679, 9.4926, -1.4116], | |
[2.2279, 4.0951, -4.8037, -2.1112], | |
[-6.1632, 8.7096, -0.21621, -3.6345], | |
[0.52374, 3.644, -4.0746, -1.9909], | |
[1.5077, 1.9596, -3.0584, -0.12243] | |
]) | |
labels = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0]) | |
return feats, labels | |
def test_dt_overfit(dummy_feats_and_labels): | |
feats, labels = dummy_feats_and_labels | |
dt = DecisionTree() | |
dt.fit(feats, labels) | |
pred = np.round(dt.predict(feats)) | |
assert np.array_equal(labels, pred), 'DecisionTree should fit data perfectly and prediction should == labels.' |
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