Naive Bayes looks at each _ predictor and creates a probability that belongs in each class.

Someone of your data science team recommends that you use decision trees, naive Bayes and K-nearest neighbor, all at the same time, on the same training data, and then average the results. What is this an example of?

Your data science team wants to use machine learning to better filter out spam messages. The team has gathered a database of 100,000 messages that have been identified as spam or not spam. If you are using supervised machine learning, what would you call this data set?

You work for a website that enables customers see all images of themselves on the internet by uploading one self-photo. Your data model uses 5 characteristics to match people to their foto: color, eye, gender, eyeglasses and facial hair. Your customers have been complaining that get tens of thousands of photos without them. What is the problem?

Your supervisor asks you to create a machine learning system that will help your human resources department classify jobs applicants into well-defined groups. What type of system are you more likely to recommend?

You and your data science team have 1 TB of example data. What do you typically do with that data?

In the SVM classif‌ication approach, which of the following options is used for solving the issue of the low-sparse SVM classifier?

In machine learning, which functions in the image is used to measure the size of vectors?

Which techniques can be used for solving the attribute conditional density estimation problem in the Bayesian Network classif‌ication method?

ln machine learning, logistic regression:

Which of the following is NOT applicable to speech recognition application in machine learning?