Question 4Which of the following statements about scaling features prior to regularization is TRUE?1 pointFeature scaling is not recommented prior to regularization.Features should rarely or never be scaled prior to implementing regularization.The larger a feature’s scale, the more likely its estimated impact will be influenced by regularization.The smaller a feature’s scale, the more likely its estimated impact will be influenced by regularization.
Question
Question 4Which of the following statements about scaling features prior to regularization is TRUE?1 pointFeature scaling is not recommented prior to regularization.Features should rarely or never be scaled prior to implementing regularization.The larger a feature’s scale, the more likely its estimated impact will be influenced by regularization.The smaller a feature’s scale, the more likely its estimated impact will be influenced by regularization.
Solution
The correct statement is: "The larger a feature’s scale, the more likely its estimated impact will be influenced by regularization."
Here's why:
Regularization is a technique used to prevent overfitting in a machine learning model by adding a penalty term to the loss function. The penalty term discourages the model from assigning too much importance to any one feature.
When features are on different scales, the larger-scaled features will have larger coefficients in the model. Regularization tends to shrink these larger coefficients more, which means the estimated impact of larger-scaled features will be more influenced by regularization.
Therefore, it's often recommended to scale features prior to regularization so that all features are on a similar scale and the regularization term can shrink all coefficients equally. This way, the model won't unfairly penalize larger-scaled features.
Similar Questions
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.
ll of the following statements about Regularization are TRUE except:1 pointOptimizing predictive models is about finding the right bias/variance tradeoff.Features should rarely or never be scaled prior to implementing regularization.We need models that are sufficiently complex to capture patterns in data, but not so complex that they overfit.Regularization techniques have an analytical, a geometric, and a probabilistic interpretation.
Which of the following statements about regularization techniques is false?Question 9AnswerA.Regularization reduces the effective number of features used by the modelB.Regularization helps to combat overfitting.C.Regularization shrinks the weights of less important features towards zero.D.Regularization increases the model bias
hich of the following(s) is/are feature scaling techniques?
Which statement under the Probabilistic View is correct?1 pointRegularization imposes certain errors on the regression coefficients. Feedback: Incorrect! Please review the further Details of Regularization lessons. Regularization imposes certain priors on the regression coefficients. Regularization uses some regression coefficients to inflate the errors. Regularization coefficients do not take into consideration prior probabilities.
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