MCQs

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Overall MCQs for Reference

What does “Bottom N” most commonly refer to in machine learning?
a. The N most important features
b. The N least important items after ranking
c. N random items
d. N outliers only
Answer: b. The N least important items after rankingkdnuggets+1
Which feature selection method can be used to perform Bottom N feature selection?
a. Recursive Feature Elimination
b. Forward Stepwise Selection
c. Random Sampling
d. K-Means Clustering
Answer: a. Recursive Feature Eliminationfrontiersin+1
Bottom N selection is particularly useful when you want to:
a. Focus on the highest performers
b. Identify underperforming items or features
c. Randomize the dataset
d. Test all features equally
Answer: b. Identify underperforming items or featuresstratascratch
In model interpretation, examining the Bottom N can help to:
a. Eliminate irrelevant features
b. Maximize computational time
c. Enhance all feature weights
d. Ignore ranking
Answer: a. Eliminate irrelevant featuresgeeksforgeeks
Which metric could be used to rank features before selecting Bottom N?
a. Gini Importance
b. Information Gain
c. Feature Weights
d. All of the above
Answer: d. All of the abovefrontiersin+2
The Bottom N process in feature selection is generally aimed at:
a. Reducing dimensionality
b. Increasing overfitting
c. Adding noisy data
d. Expanding the dataset
Answer: a. Reducing dimensionalitystratascratch+1
In Decision Trees, Bottom N features often have:
a. High split importance
b. Low split importance
c. Highest correlation
d. No effect on splits
Answer: b. Low split importancefrontiersin
Why might you want to analyze the Bottom N samples in a prediction output?
a. To reward best performance
b. To understand worst-case scenarios
c. To increase false positives
d. For feature expansion
Answer: b. To understand worst-case scenariosfrontiersin
In fraud detection, what could analyzing Bottom N transactions reveal?
a. The most secure activities
b. Potentially high-risk anomalies
c. Repeated transactions
d. Largest transaction amounts
Answer: b. Potentially high-risk anomaliesgeeksforgeeks
What machine learning technique may utilize Bottom N logic for feature pruning?
a. Lasso Regression
b. K-Means
c. Apriori Algorithm
d. K-Nearest Neighbors
Answer: a. Lasso Regressionkdnuggets
The Bottom N in clustering analysis often refers to:
a. The largest clusters
b. Clusters with fewest points
c. Clusters nearest to centroid
d. Most dense clusters
Answer: b. Clusters with fewest pointsgeeksforgeeks+1
When visualizing model residuals, what can selecting the Bottom N residuals show?
a. Where the model performs best
b. Where the model performs worst
c. Features with highest importance
d. The entire data range
Answer: b. Where the model performs worststratascratch
Which algorithm could directly rank features for Bottom N elimination?
a. Random Forest
b. Naive Bayes
c. Agglomerative Clustering
d. Linear Discriminant Analysis
Answer: a. Random Forestgeeksforgeeks+1
Bottom N error analysis is helpful for:
a. Tuning hyperparameters
b. Understanding edge cases
c. Generating new data
d. Normalizing datasets
Answer: b. Understanding edge casesstratascratch
Which wrapper method relies on recursively removing Bottom N features?
a. Principal Component Analysis
b. Recursive Feature Elimination
c. K-Fold Cross Validation
d. Bagging
Answer: b. Recursive Feature Eliminationgeeksforgeeks+1
What is a risk of always removing Bottom N features in every ML project?
a. Losing useful information
b. Algorithm bias
c. Always increasing accuracy
d. No effect on outcome
Answer: a. Losing useful informationfrontiersin+1
Analyzing Bottom N predictions could help with:
a. Error diagnosis
b. Increasing accuracy only
c. Feature selection only
d. None of the above
Answer: a. Error diagnosisstratascratch
If you repeatedly drop the Bottom N features and retrain, this is called:
a. Forward selection
b. Backward elimination
c. Cross-validation
d. Random forest
Answer: b. Backward eliminationstratascratch
What does Bottom N mean in a business dashboard using ML outputs?
a. The N largest sales
b. The N smallest sales
c. All sales combined
d. The median sales
Answer: b. The N smallest salesfrontiersin
When analyzing ML feature importance scores, Bottom N are:
a. Most significant
b. Least significant
c. Unchanged
d. Always zero
Answer: b. Least significantgeeksforgeeks+2
In recommendation systems, Bottom N recommendations are typically:
a. Most viewed
b. Least relevant
c. Most popular
d. Always random
Answer: b. Least relevantstratascratch
Bottom N can help detect which of the following in a dataset?
a. Underrepresented classes
b. Overfitting only
c. Optimal features
d. Random noise
Answer: a. Underrepresented classesfrontiersin
What does Bottom N sample selection help achieve?
a. Robustness to rare cases
b. Only model speed
c. More overfitting
d. Data duplication
Answer: a. Robustness to rare casesstratascratch
In correlation-based feature ranking, Bottom N refer to features with:
a. Highest correlation
b. Lowest correlation
c. Zero importance
d. Highest missing values
Answer: b. Lowest correlationstratascratch
Assessing Bottom N features can aid in reducing:
a. Multicollinearity
b. Model complexity
c. Both a and b
d. None of the above
Answer: c. Both a and bgeeksforgeeks
In outlier detection, Bottom N residuals typically indicate:
a. Most normal observations
b. Largest errors
c. Average samples
d. Dominant clusters
Answer: b. Largest errorsgeeksforgeeks+1
Which ML library provides utilities for Bottom N feature selection?
a. pandas
b. sklearn.feature_selection
c. numpy
d. matplotlib
Answer: b. sklearn.feature_selectionkdnuggets+1
Which of the following is a valid strategy for Bottom N class balancing?
a. Downsampling majority class
b. Oversampling minority class
c. Both
d. None
Answer: c. Bothstratascratch
Bottom N outputs in anomaly detection may indicate:
a. Most significant fraud
b. Trivial data
c. Noise
d. Least suspicious points
Answer: a. Most significant fraudgeeksforgeeks
What is Bottom N accuracy in a Top-N type evaluation?
a. Accuracy for lowest ranked predictions
b. Overall model accuracy
c. Random accuracy
d. Highest ranking accuracy
Answer: a. Accuracy for lowest ranked predictionsgeeksforgeeks
When using the RFE algorithm, the Bottom N features are:
a. Always selected first
b. Removed first
c. Scaled by standard deviation
d. Always continuous variables
Answer: b. Removed firstkdnuggets+2
Bottom N analysis is LEAST useful for?
a. Identifying model weaknesses
b. Selecting best options
c. Outlier analysis
d. Model debugging
Answer: b. Selecting best optionsfrontiersin
In customer churn analysis, Bottom N customers are:
a. Most loyal
b. Highest churn risk
c. Random picks
d. Most profitable
Answer: b. Highest churn riskstratascratch
Why is Bottom N feature importance sometimes misleading?
a. Ranking may change after retraining
b. Features with complex interactions may be overlooked
c. All of the above
d. Bottom rankings are always consistent
Answer: c. All of the abovefrontiersin
What does analyzing Bottom N outcomes help avoid in ML projects?
a. Ignoring edge cases
b. Improving best cases
c. Scaling problems
d. None
Answer: a. Ignoring edge casesstratascratch
Bottom N threshold in ranked outputs should be set based on:
a. Arbitrary choices
b. Domain expertise
c. Always 10%
d. Random seed
Answer: b. Domain expertisefrontiersin
After removing Bottom N features, what’s ideal to do next?
a. Retrain model and check metrics
b. Increase feature count
c. Stop training
d. Randomize data
Answer: a. Retrain model and check metricskdnuggets+1
Bottom N can be computed for:
a. Feature importance
b. Error rates
c. Sample predictions
d. All of the above
Answer: d. All of the abovefrontiersin+1
Which is an example of Bottom N in ensemble methods?
a. Voting for highest accuracy
b. Removing least contributing weak learners
c. Increasing tree depth
d. Averaging outputs
Answer: b. Removing least contributing weak learnersgeeksforgeeks
What is typically true of Bottom N clusters in K-means?
a. Have the most members
b. Often contain outliers
c. Always spherical
d. Least variable
Answer: b. Often contain outliersfrontiersin
Bottom N is often used during:
a. Final model selection
b. Exploratory data analysis (EDA)
c. Both
d. None
Answer: c. Bothfrontiersin+1
In Lasso regression, Bottom N coefficients:
a. Are shrunk to zero
b. Are always negative
c. Are maximized
d. Are integers
Answer: a. Are shrunk to zerokdnuggets
When visualizing Bottom N predictions, which plot is useful?
a. Box plot
b. Residual plot
c. Both
d. None
Answer: c. Bothstratascratch
Which step can follow identification of Bottom N features?
a. Elimination from the dataset
b. Feature engineering
c. Both
d. None
Answer: c. Bothgeeksforgeeks+1
What is a potential use of Bottom N user ratings in recommender systems?
a. Improve recommendations by excluding or learning from low-rated interactions
b. Always show Bottom N to users
c. Ignore all feedback
d. Replace all ratings with median
Answer: a. Improve recommendations by excluding or learning from low-rated interactionsstratascratch
What can be said of Bottom N values in feature importance?
a. Low impact on target variable
b. Highest variance always
c. Always categorical
d. Never change
Answer: a. Low impact on target variablegeeksforgeeks+1
A report emphasizing Bottom N model outputs helps highlight:
a. Model weaknesses and improvement areas
b. Best strengths only
c. Data duplication
d. Largest errors only
Answer: a. Model weaknesses and improvement areasstratascratch
Which real-world use-case is likely to require Bottom N as part of analysis?
a. Fraud detection
b. Customer churn monitoring
c. Medical misdiagnosis analysis
d. All of the above
Answer: d. All of the abovegeeksforgeeks+2
What is a limitation of only focusing on Bottom N for feature removal?
a. May remove contextually relevant features
b. Always improves test accuracy
c. Reduces reproducibility
d. Increases data leakage
Answer: a. May remove contextually relevant featuresgeeksforgeeks+1
For time series anomaly detection, Bottom N scores might indicate:
a. Most significant anomalies
b. Least significant events
c. Largest average trend
d. Random behavior
Answer: a. Most significant anomaliesgeeksforgeeks+1

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