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f(x) = a1x1 +a2x2 + .. + anxn + b;
y = f(x) = a1x_1 + a2x_2 + ... + anx_n + b
a1, a2, ..., an, b
{x^(1), y^(1)}, {x^(2), y^(2)}..., {x^(100), y^(100)}
from sklearn import datasets, linear_model # fit the model by Linear Regression regr = linear_model.LinearRegression() regr.fit(X, y) // Step 3 y_prediction = regr.predict(X_new) // Step 4
2.2K Point(s)
1.2K Point(s)
313 Point(s)
178 Point(s)
138 Point(s)
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