What Are Correct About Choosing L1 Regularization Lasso Over L2 Regularization Ridge It Is Faster To Learn The Weig 1 (65.52 KiB) Viewed 69 times
What Are Correct About Choosing L1 Regularization Lasso Over L2 Regularization Ridge It Is Faster To Learn The Weig 2 (29.59 KiB) Viewed 69 times
What are correct about choosing L1 regularization (LASSO) over L2 regularization (Ridge): It is faster to learn the weights for L1 regularization. L1 regularization can help us identify which features are important for a certain task. L1 regularization usually achieves lower generalization error. If there are many features, running predictions using models trained with L1 regularization is more computationally efficient.
What of the following models cannot guarantee a globally optimal result? Logistic Regression Decision Tree K-Means Clustering Neural Networks
What of the following are unique to Gaussian mixture model (GMM) in comparison with k-means clustering (KMC)? GMM requires pre-defining a specific number of clusters before running the algorithm, but KMC does not. GMM calculates the mean and covariance of each cluster, but KMC does not. GMM allows clusters that can potentially overlap, but KMC does not. GMM can have different cluster results from different initializations, but KMC cannot.
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