AI ML Quiz 2 Unit II S2+ L4

What are the different types of attributes?

• Nominal

• Ordinal

• Spacial

• All of the Above

What are some examples of data quality problems:

• Noise and outliers

• Duplicate data

• Missing values

• All of the Above

Which Method is used for encoding the categorical variables?

• LabelEncoder

• OneHotEncoder

• OneHotEncoder

• All of the Above

The correct way of pre processing the data should be-

• Imputation ->feature scaling-> training

• Feature scaling->imputation->training

• Feature scaling->label encoding->training

• None

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

• Removing columns that have too many missing values

• Removing columns that have high variance in data

• Removing columns with dissimilar data trends

• None of these

LDA is an example of supervised dimensionality reduction algorithm.

• True

• False

Imagine, you have 1000 input features and 1 target feature in a machine learning problem. You have to select 100 most important features based on the relationship between input features and the target features.

Do you think, this is an example of dimensionality reduction?

• True

• False

The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA?

1. 1. PCA is an unsupervised method of optimization

2. 2. It searches for the directions that data have the largest variance

3. 3. Maximum number of principal components <= number of features

4. 4. All principal components are orthogonal to each other

• 1 and 2

• 1 and 3

• 2 and 3

• 1,2,3 and 4

Which of the following is true about LDA?

• LDA aims to maximize the distance between classes and minimize the within-class distance.

• LDA aims to minimize both distances between classes and the distance within the class.

• LDA aims to minimize the distance between classes and maximize the distance within the class.

• LDA aims to maximize both distances between classes and the distance within the class.

For k cross-validation, larger k value implies more bias.

• True

• False