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

question 43

Multiple Choice

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)

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Definitions:

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Sherman Act

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