Question 3Which of the following statements about regularization is TRUE? 1 pointRegularization always reduces the number of selected features. Regularization increases the likelihood of overfitting relative to training data. Regularization decreases the likelihood of overfitting relative to training data.Regularization performs feature selection without a negative impact in the likelihood of overfitting relative to the training data.
Question
Question 3Which of the following statements about regularization is TRUE? 1 pointRegularization always reduces the number of selected features. Regularization increases the likelihood of overfitting relative to training data. Regularization decreases the likelihood of overfitting relative to training data.Regularization performs feature selection without a negative impact in the likelihood of overfitting relative to the training data.
Solution
The correct statement about regularization is: "Regularization decreases the likelihood of overfitting relative to training data."
Here's why:
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Regularization does not always reduce the number of selected features. It adds a penalty to the loss function, which can lead to some coefficients becoming zero (in the case of L1 regularization), but it doesn't necessarily reduce the number of features.
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Regularization does not increase the likelihood of overfitting; in fact, it's used to prevent overfitting. Overfitting occurs when a model learns the training data too well, to the point where it performs poorly on unseen data. Regularization adds a penalty to the loss function to prevent the model from fitting too closely to the training data.
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Regularization does decrease the likelihood of overfitting relative to training data. This is because it adds a penalty to the loss function, which discourages the model from fitting too closely to the training data.
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Regularization can perform feature selection (in the case of L1 regularization), but it can have a negative impact on the likelihood of overfitting if the penalty is too large. This is because it could cause the model to underfit the data.
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