In the Context of the California Housing Dataset, Which of the Following

In the context of the California Housing dataset, which of the following statements is false?

A) The following code creates a LinearRegression estimator and invokes its xe "scikit-learn (sklearn) machine-learning library:fit method of an estimator"xe "fit method:of a scikit-learn estimator"fit method to train the estimator using X_train (the samples) and y_train (the targets) : from sklearn.linear_model import LinearRegression
Linear_regression = LinearRegression()
Linear_regression.fit(X=X_train, y=y_train)
B) Multiple linear regression produces separate coefficients for each feature (stored in coeff_) in the dataset and one intercept (stored in intercept_) .
C) For positive coefficients, the median house value increases as the feature value increases. For negative coefficients, the median house value decreases as the feature value decreases.
D) You can use the coefficient and intercept values with the following equation to make predictions: y = m₁x₁ + m₂x₂ + … m_nx_n + b
Where
$\bullet$ m₁, m₂, …, m_n are the feature coefficients
$\bullet$ b is the intercept
$\bullet$ x₁, x₂, …, x_n are the feature values (that is, the values of the independent variables)
$\bullet$ y is the predicted value (that is, the xe "dependent variable"dependent variable)