Dimensionality Reduction Techniques

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?

• Yes

• No

It is not necessary to have a target variable for applying dimensionality reduction algorithms.

• True

• False

I have 4 variables in the dataset such as - A, B, C & D. I have performed the following actions:

Step 1: Using the above variables, I have created two more variables, namely E = A + 3 B and F = B + 5 C + D.

Step 2: Then using only the variables E and F I have built a Random Forest model.

Could the steps performed above represent a dimensionality reduction method?

• No

• Yes

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

• Removing columns which have too many missing values

• Removing columns which have high variance in data

• Removing columns with dissimilar data trends

• None of these

Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.

• True

• False

Which of the following algorithms cannot be used for reducing the dimensionality of data?

• Factor Analysis

• PCA

• LDA

• None of these

PCA can be used for projecting and visualizing data in lower dimensions.

• True

• Fasle

What are the feature extraction techniques ?

• Filter, Wrapper and Hybrid

• Extractor, Hardware and Software

Filter Method is more complex than Wrapper methods?

• Yes

• No

Hybrid method involves evaluation at each iteration?

• True

• False