Machine learning Quiz # 7

Instructions
Quiz: Machine learning Quiz # 7
Total Questions: 30 MCQs
Time: 30 Minutes

Note

  • Do not refresh the page while taking the test.
  • Results along with correct answers will be shown at the end of the test.
Machine Learning Quiz # 7
Question 1 of 30
00:00
  • Compared to the variance of the Maximum Likelihood Estimate (MLE), the variance of the Maximum A Posteriori (MAP) estimate is ___

  • ___ refers to a model that can neither model the training data nor generalize to new data.

  • What does it mean to underfit your data model?

  • Asian user complains that your company's facial recognition model does not properly identify their facial expressions. What should you do?

  • You work for a website that helps match people up for lunch dates. The website boasts that it uses more than 500 predictors to find customers the perfect date, but many costumers complain that they get very few matches. What is a likely problem with your model?

  • (Mostly) whenever we see kernel visualizations online (or some other reference) we are actually seeing:

  • The activations for class A, B and C before softmax were 10,8 and 3. The different in softmax values for class A and class B would be :

  • The new dataset you have just scraped seems to exhibit lots of missing values. What action will help you minimizing that problem?

  • Which of the following methods can use either as an unsupervised learning or as a dimensionality reduction technique?

  • What is the main motivation for using activation functions in ANN?

  • Which loss function would fit best in a categorical (discrete) supervised learning ?

  • You create a decision tree to show whether someone decides to go to the beach. There are three factors in this decision: rainy, overcast, and sunny. What are these three factors called?

  • You need to quickly label thousands of images to train a model. What should you do?

  • You need to select a machine learning process to run a distributed neural network on a mobile application. Which would you choose?

  • Which choice is the best example of labeled data?

  • In statistics, what is defined as the probability of a hypothesis test of finding an effect - if there is an effect to be found?

  • You want to create a machine learning algorithm to identify food recipes on the web. To do this, you create an algorithm that looks at different conditional probabilities. So if the post includes the word flour, it has a slightly stronger probability of being a recipe. If it contains both flour and sugar, it even more likely a recipe. What type of algorithm are you using?

  • What is lazy learning?

  • What is Q-learning reinforcement learning?

  • The data in your model has low bias and low variance. How would you expect the data points to be grouped together on the diagram?

  • Your machine learning system is using labeled examples to try to predict future data, compare that data to the predicted result, and then the model. What is the best description of this machine learning method?

  • In the 1983 movie WarGames, the computer learns how to master the game of chess by playing against itself. What machine learning method was the computer using?

  • You are working with your machine learning algorithm on something called class predictor probability. What algorithm are you most likely using?

  • What is one of the most effective way to correct for underfitting your model to the data?

  • Your data science team is often criticized for creating reports that are boring or too obvious. What could you do to help improve the team?

  • What is the difference between unstructured and structured data?

  • You work for a startup that is trying to develop a software tool that will scan the internet for pictures of people using specific tools. The chief executive is very interested in using machine learning algorithms. What would you recommend as the best place to start?

  • In supervised machine learning, data scientist often have the challenge of balancing between underfitting or overfitting their data model. They often have to adjust the training set to make better predictions. What is this balance called?

  • What is conditional probability?

  • K-means clustering is what type of machine learning algorithm?