Your company wants to predict whether existing automotive insurance customers are more likely to buy homeowners insurance. It created a model to better predict the best customers contact about homeowners insurance, and the model had a low variance but high bias. What does that say about the data model?

You want to identify global weather patterns that may have been affected by climate change. To do so, you want to use machine learning algorithms to find patterns that would otherwise be imperceptible to a human meteorologist. What is the place to start?

You work in a data science team that wants to improve the accuracy of its K-nearest neighbor result by running on top of a naive Bayes result. What is this an example of?

____ looks at the relationship between predictors and your outcome.

What is an example of a commercial application for a machine learning system?

You work for a power company that owns hundreds of thousands of electric meters. These meters are connected to the internet and transmit energy usage data in real-time. Your supervisor asks you to direct project to use machine learning to analyze this usage data. Why are machine learning algorithms ideal in this scenario?

The EIasticNet regression technique:

Output of following code executed in MATLAB. m = roots([?., 8])

Which statistical data mining techniques is used to predict a categorical response variable?

Output of code in MATLAB? z = [6 9 4 3 5]: polyval(z,3)

In relation to machine learning classif‌ication, which of following options refers to the graphical model for probability associations between a set of variables?