Q1easy
Looking at this transformation, what is the effect on the data?
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11 Questions Total
Feature Engineering & Selection MCQs
Topic-wise practice questions for Artificial Intelligence & Machine Learning. Test your knowledge, check solutions on demand, and pace your preparation.
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Practice in Timed ModeQ2easy
What is the main drawback of using Wrapper Methods for feature selection?
Q3easy
If you have a column "Date" (e.g., 2023-01-01), what is a valid feature engineering step?
Q4medium
In Pandas, what is the best way to convert a categorical column 'Gender' (Male, Female) into numeric columns?
Q5easy
What is the purpose of the L1 Regularization (Lasso) during training?
feature_eng.py
import pandas as pd
df = pd.DataFrame({'Gender': ['Male', 'Female']})
# What command performs one-hot encoding?
Q6easy
What is a Dummy Variable Trap?
Q7easy
In feature engineering, what does Binning achieve?
Q8medium
A dataset has 500 features, but many are highly correlated with each other. Which technique should you use to reduce the dimensionality?
Q9medium
Why is One-Hot Encoding generally preferred over Label Encoding for nominal categorical data (e.g., Colors: Red, Green, Blue)?
Q10easy
What is the difference between Filter Methods and Wrapper Methods for feature selection?