ML quiz 3

Data augmentation can be used at train and test time.

• True

• False

The most widely used metrics and tools to assess a classification model are:

• Confusion matrix

• Cost-sensitive accuracy

• Area under the ROC curve

• All of the above

Which of the following is a good test dataset characteristic?

• Large enough to yield meaningful results

• Is representative of the dataset as a whole

• Both

• None

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

• None

What is the purpose of performing cross-validation?

• To assess the predictive performance of the models

• To judge how the trained model performs outside the sample on test data

• All of the above

Which of the folllowing is an example of feature extraction?

• Constructing bag of words vector from an email

• Applying PCA projects to a large high-dimensional data

• Removing stopwords in a sentence

• All of the above

• None

Imagine, you want to develop a machine learning algorithm which predicts the number of views on the articles. Your analysis is based on features like author name, number of articles written by the same author in past and a few other features. Which of the following evaluation metric would you choose in that case?

1. Mean Square Error

2. Accuracy

3. F1 Score

• 1

• 3

• 2

• 1 & 2

• 2 & 3

Below are the 8 actual values of target variable in the train file.

[0,0,0,1,1,1,1,1]

What is the entropy of the target variable?

• -(5/8 log(5/8) + 3/8 log(3/8))

• 5/8 log(5/8) + 3/8 log(3/8)

• 3/8 log(5/8) + 5/8 log(3/8)

• 5/8 log(3/8) – 3/8 log(5/8)

Let’s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data.You want to apply one hot encoding (OHE) on the categorical feature(s).

What challenges you may face if you have applied OHE on a categorical variable of train dataset?

• All categories of categorical variable are not present in the test dataset.

• Frequency distribution of categories is different in train as compared to the test dataset.

• Train and Test always have same distribution.

• Both A and B

• None of these

LogLoss evaluation metric can have negative values.

• True

• False