Your data science team must build a binary classifier, and the number one criterion is the fastest possible scoring at deployment. It may even be deployed in real time. Which technique will produce a model that will likely be fastest for the deployment team use to new cases?

Your data science team wants to use the K-nearest neighbor classification algorithm. Someone on your team wants to use a K of 25. What are the challenges of this approach?

Your machine learning system is attempting to describe a hidden structure from unlabeled data. How would you describe this machine learning method?

You work for a large credit card processing company that wants to create targeted promotions for its customers. The data science team created a machine learning system that groups together customers who made similar purchases, and divides those customers based on customer loyalty. How would you describe this machine learning approach?

You are using K-nearest neighbor and you have a K of 1. What are you likely to see when you train the model?

Are data model bias and variance a challenge with unsupervised learning?

Which of the following is incorrect about linear regression?

In relation to machine learning framework, which of the following languages is used by Veles for performing automation and coordination between the nodes?

Which of the following is an advantage in logistic regression?

In the knowledge discovery process, which of the following steps is involved in retrieving data, relevant to the analysis task, from the database?

In relation to K-Nearest Neighbors (K-NN) algorithm, what effect does the small k value (neighborhood size) have on bias and variance?