ln machine learning, logistic regression:

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

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

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

Which of the following defines the F-score measure to assess the quality of text retrieval?

Which tools is used for processing, analyzing and visualizing the large data sets and can provide native support for Apache Spark distributed computing?

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 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?

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?

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

Many of the advances in machine learning have come from improved ___.