PCA is typically used to reduce the number of dimensions of…

PCA is typically used to reduce the number of dimensions of a machine learning problem. For example, we might go from 20 features to just using the top 10 components identified by PCA. Intuitively, this would tell us that we are throwing away some information. But, strangely, when the dataset it noisy, throwing away some of the low PCA components might get us better results. Why is this the case?

Given the following code snippet: import numpy as np matrix…

Given the following code snippet: import numpy as np matrix = np.eye(3, 3) * 4 + np.eye(3, 3, k=-1) * 5 + np.eye(3, 3, k=1) * 3 Fill in the entries of matrix Note: Write whole numbers only Do not write any decimal points Do not write any square/curly brackets/parenthesis

You are an experienced machine learning engineer and you’ve…

You are an experienced machine learning engineer and you’ve been given a data set and are told to create a machine learning model to predict the classes. The customer is already using a competitor’s model and are happy with it but your management team has offered a lower price. You attempt to use your company’s most successful machine learning algorithm, Perceptron, but have been unable to get acceptable results. It finally occurs to you to use a different model and you succeed. What did you realize?