Esempi in Python per mae

Esempio n. 1

File: factorization_machine_regressor_test.py Progetto: marvelousNinja/mlkit-py

def test_on_boston():
    return
    X, y = load_boston()
    model = FactorizationMachineRegressor(k=2)
    model.fit(X[:400], y[:400])
    y_pred = model.predict(X[400:])
    assert mae(y[400:], y_pred) == pytest.approx(3.9, 0.1)

Esempio n. 2

File: linear_regressor_test.py Progetto: marvelousNinja/mlkit-py

def test_on_boston():
    X, y = load_boston()
    scale = make_scaler(X[:400])
    model = LinearRegressor()
    model.fit(scale(X[:400]), y[:400])
    y_pred = model.predict(scale(X[400:]))
    assert mae(y[400:], y_pred) == pytest.approx(5, 0.1)

Esempio n. 3

def test_on_boston():
    X, y = load_boston()
    model = DecisionTreeRegressor()
    model.fit(X[:400], y[:400])
    y_pred = model.predict(X[400:])
    assert mae(y[400:], y_pred) == pytest.approx(4.0, 0.1)

Esempio n. 4

File: extra_trees_regressor_test.py Progetto: marvelousNinja/mlkit-py

def test_on_boston():
    X, y = load_boston()
    model = ExtraTreesRegressor()
    model.fit(X[:400], y[:400])
    y_pred = model.predict(X[400:])
    assert mae(y[400:], y_pred) == pytest.approx(3.9, 0.1)

Esempio n. 5

File: random_forest_regressor_test.py Progetto: marvelousNinja/mlkit-py

def test_on_boston():
    X, y = load_boston()
    model = RandomForestRegressor()
    model.fit(X[:400], y[:400])
    y_pred = model.predict(X[400:])
    assert mae(y[400:], y_pred) == pytest.approx(3.2, 0.1)

Esempio n. 6

def test_on_boston():
    X, y = load_boston()
    model = GradientBoostingRegressor()
    model.fit(X[:400], y[:400])
    y_pred = model.predict(X[400:])
    assert mae(y[400:], y_pred) == pytest.approx(4.3, 0.1)