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Question 110/112
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?
Correct Answer: B
L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the model's coefficients to the loss function1. It encourages sparsity in the model by shrinking some coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove the non-informative features from the model while keeping the informative ones in their original form. Therefore, using L1 regularization is the best technique for this use case.
References:
* Regularization in Machine Learning - GeeksforGeeks
* Regularization in Machine Learning (with Code Examples) - Dataquest
* L1 And L2 Regularization Explained & Practical How To Examples
* L1 and L2 as Regularization for a Linear Model
References:
* Regularization in Machine Learning - GeeksforGeeks
* Regularization in Machine Learning (with Code Examples) - Dataquest
* L1 And L2 Regularization Explained & Practical How To Examples
* L1 and L2 as Regularization for a Linear Model
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