Machine Learning Quiz

Which of the following are common classes of problems in machine learning?

• Regression

• Classification

• Clustering

• ALL the Above

Which of the following machine learning algorithm is based upon the idea of bagging?

• Regression

• Classification

• Radom Forest

• Decision Tree

What is one reason not to use the same data for both your training set and your testing set?

• You will almost certainly underfit the model.

• You will pick the wrong algorithm.

• You will almost certainly overfit the model.

• You might not have enough data for both.

To predict a quantity value. use ___

• Regression

• Clustering

• Classification

• Dimenstionality Reduction

How do you handle missing or corrupted data in a dataset?

• Drop missing rows or columns

• Replace missing values with mean/median/mode

• Assign a unique category to missing values

• All of the above

Which evaluation metric is commonly used for binary classification problems and measures the proportion of true positive predictions among all positive examples?

• Accuracy

• F1 Score

• Recall

• Precision

Which of the following techniques would perform better for reducing dimensions of a data set?

• removing columns with dissimilar data trends

• removing columns which have high variance in data

• removing columns which have too many missing value

• None of the Above

Impact of high variance on the training set ?

• Underfitting

• Overfitting

• Both Underfit and Overfit

• depends on the data set

Of the Following Examples, Which would you address using an supervised learning Algorithm?

• given a set of news articles found on the web, group them into set of articles about the same story

• find the patterns in market basket analysis

• given a database of customer data, automatically discover market segments and group customers into different market segments

• given email labeled as spam or not spam, learn a spam filter

The effictiveness of the SVM depends upon

• Kernel parameters

• selection of kernel

• soft margin parameter

• all the above