scikit-learn में मल्टी-आउटपुट रिग्रेशन का मूल्यांकन करना

from sklearn.metrics import mean_absolute_error; import numpy as np; y_true = np.array([[0.5, 1], [-1, 1], [7, -6]]); y_pred = np.array([[0, 2], [-1, 2], [8, -5]]); scores = mean_absolute_error(y_true, y_pred, multioutput='raw_values'); print(scores) # Illustrative output: [0.5, 1. ]

from sklearn.metrics import mean_absolute_error; import numpy as np; y_true = np.array([[0.5, 1], [-1, 1], [7, -6]]); y_pred = np.array([[0, 2], [-1, 2], [8, -5]]); weighted_mae = mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7]); print(weighted_mae) # Illustrative output: 0.85

from sklearn.metrics import make_scorer, mean_absolute_error; from sklearn.model_selection import GridSearchCV; from sklearn.linear_model import LinearRegression; weighted_mae_scorer = make_scorer(mean_absolute_error, multioutput=[0.3, 0.7], greater_is_better=False); grid = GridSearchCV(LinearRegression(), param_grid={}, scoring=weighted_mae_scorer)

from sklearn.model_selection import cross_validate; from sklearn.metrics import make_scorer, mean_absolute_error; from sklearn.linear_model import LinearRegression; import numpy as np; X = np.random.rand(10, 3); y = np.random.rand(10, 2); scoring_dict = {'weighted_mae': make_scorer(mean_absolute_error, multioutput=[0.3, 0.7], greater_is_better=False), 'r2': 'r2'}; results = cross_validate(LinearRegression(), X, y, scoring=scoring_dict, cv=2); print(results['test_weighted_mae'])