Muhammad Imdad Ullah

Master Machine Learning MCQS fundamentals with this interactive quiz! The machine learning MCQs test covers supervised learning, neural networks, and recommendation systems.. Test your understanding of key topics like labeled datasets, unsupervised learning, recommender systems, and gradient boosting. Take the Machine Learning MCQs Quiz and level up your skills!

Online machine learning mcqs with Answers

1. In recommendation systems, what term describes the phenomenon of more well-known items being recommended too frequently?

 Fame factor

 Popularity bias

 Non-objectivity

 Trend partiality

2. A neural network is best suited for

 None of these

 Linear relationships only

 Complex patterns (e.g., image recognition)

 Tabular data with few features

3. Supervised learning requires

 Rewards/punishments for actions

 Only input features (no labels)

 Labeled training data

 Clustering unlabeled data

4. Which term best describes the statement, “a subset of AI that uses computer algorithms to analyze data and make intelligent decisions based on what it has learned without being explicitly programmed”?

 Big data

 Machine learning

 Data scientist

 Distributed network

5. Which of the following statements correctly describes gradient boosting?

 Gradient boosting machines build models in parallel

 Gradient boosting machines do not require the data to be scaled

 Gradient boosting machines work well with missing data

 Gradient boosting machines tell you the coefficients for each feature

6. Content-based filtering is a recommendation system in which recommendations are made based on —————- of the attributes of the content.

 Improvements

 Segmentations

 Differentiations

 Comparisons

7. What term describes the subclass of machine learning algorithms that offers relevant suggestions to users?

 Recommendation systems

 Sensor technique

 Data models

 Suggestion maps

8. Which approach to machine learning involves rewarding or punishing a computer’s behaviors?

 Supervised machine learning

 Artificial intelligence

 Reinforcement learning

 Deep learning

9. Unstructured data includes

 Text, audio, and images

 CSV files

 Relational databases

 Spreadsheets with labeled columns

10. What are some benefits of boosting?

 Boosting functions well even with multicollinearity among the features

 Boosting is robust to outliers

 The models used in boosting can be trained in parallel across many different servers

 Boosting does not require the data to be normalized

11. Which is an example of unsupervised learning?

 Linear regression

 K-means clustering

 Sentiment analysis

 Object detection

12. Recommender systems (e.g., Netflix) use

 Collaborative filtering

 Reinforcement learning

 Only rule-based algorithms

 Time-series forecasting

13. Decision trees are used for

 Dimensionality reduction

 Only regression tasks

 Clustering

 Classification and regression

14. When several users actively like or dislike content by rating it or giving it a review, this enables ————– filtering.

 Collaborative

 Preferential

 Merit-based

 Crowdsourced

15. What are some benefits of boosting?

 Boosting is a powerful predictive methodology

 Boosting can handle both numeric and categorical features

 Boosting is the most interpretable model methodology

 Boosting does not require the data to be scaled

16. Machine learning involves using algorithms and ————— to teach computer systems to analyze and discover patterns in data.

 Computer software

 Dynamic reports

 Decision-support systems

 Statistical models

17. Supervised machine learning uses labeled datasets to train ————– to classify or predict outcomes

 Clusters

 Networks

 Algorithms

 Dashboards

18. Structured data refers to

 Organized data in tables (e.g., Excel, SQL databases)

 Only image and video data

 Data without a predefined format (e.g., social media posts)

 Data stored in JSON files

19. Which of the following statements correctly describes gradient boosting?

 Each base learner in the sequence is built to predict the residual errors of the model that preceded it

 Gradient boosting machines can be difficult to interpret

 Gradient boosting machines have difficulty with extrapolation

 Gradient boosting models can be trained in parallel

20. ML can automate

 Resume screening

 Manufacturing defect detection

 Lead scoring in sales

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Question 1 of 20

Online Machine Learning MCQs with Answers

• Structured data refers to
• Unstructured data includes
• Supervised learning requires
• Which is an example of unsupervised learning?
• ML can automate
• Recommender systems (e.g., Netflix) use
• Decision trees are used for
• A neural network is best suited for
• Supervised machine learning uses labeled datasets to train ————– to classify or predict outcomes
• Machine learning involves using algorithms and ————— to teach computer systems to analyze and discover patterns in data.
• Which approach to machine learning involves rewarding or punishing a computer’s behaviors?
• Content-based filtering is a recommendation system in which recommendations are made based on —————- of the attributes of the content.
• What term describes the subclass of machine learning algorithms that offers relevant suggestions to users?
• When several users actively like or dislike content by rating it or giving it a review, this enables ————– filtering.
• In recommendation systems, what term describes the phenomenon of more well-known items being recommended too frequently?
• What are some benefits of boosting?
• Which of the following statements correctly describes gradient boosting?
• What are some benefits of boosting?
• Which of the following statements correctly describes gradient boosting?
• Which term best describes the statement, “a subset of AI that uses computer algorithms to analyze data and make intelligent decisions based on what it has learned without being explicitly programmed”?

R Language Frequently Asked Questions

Test your AI knowledge with Generative AI MCQs covering SQL query generation, data visualization, storytelling tools, bias detection, data augmentation, and ethical AI practices. Perfect for interviews, exams, and upskilling in AI, ChatGPT, and machine learning. Boost your expertise today! Ace exams, interviews, and AI projects with practical quiz questions! Let us start with the Online Generative AI MCQs now.

Online Generative AI MCQs with Answers

• Which of the following SQL queries will be generated if you type “What is the count of rows in the Boston_house_price data set” in the prompt box of a generative AI SQL query tool?
• Which is the most suitable prompt to generate a bar chart showing the average sales by area?
• Which tool can create graphs and charts with GPT-4 through specialized prompt engineering?
• You want to use a storytelling tool that enables you to use conversational prompts to enhance content creation and refine narrative elements, such as titles, headings, bullet points, introductions, and conclusions. Which tool would be most appropriate?
• Which aspect of storytelling helps to give your narrative a clear and consistent structure?
• What is a key consideration under model considerations?
• Which technique can be used to check for biased loan approvals in the finance industry?
• Which feature of ChatGPT is used in dashboard creation?
• Which tool automatically cleans the inconsistencies and offers auto-analysis and customizable dashboards with GDPR/PDPA-compliant features?
• Which storytelling tool converts text-based documents, PDFs, or URLs into slides?
• Which aspect of storytelling helps to explain the relevance of data to the goals?
• Which ethical technique should data scientists use to check the data for potential biases in the finance industry?
• Training and running generative AI models can be computationally expensive, requiring specialized hardware and software infrastructure. Under which category does this challenge come?
• Which generative AI tool uses data augmentation techniques to improve machine learning models’ performance?
• How does generative AI enhance the extraction of insights from data?
• Which of the following can enhance data preparation with generative AI?
• Which of the following is the definition of ‘data augmentation’?
• Which generative AI tool can augment structured data sets?
• Which prompt can you use for the following query: SELECT AVG(AGE) FROM Boston_house_price
• Which of the following tools can be used to augment image data sets by generating high-resolution, realistic images?

Special Values in R Language

Learn key strategies for feature selection in machine learning with this comprehensive Q&A guide. Discover methods to identify important variables, handle datasets with >30% missing values, and tackle high-dimensional data ($p > n$). Understand why OLS fails and explore better alternatives like Lasso, XGBoost, and imputation techniques. Perfect for data scientists optimizing machine learning models! Let us start with feature selection in machine learning.

Feature Selection in Machine Learning

While working on a dataset, how do you select important variables? Discuss the methods.

Selecting important variables (feature selection in machine learning) is crucial for improving model performance, reducing overfitting, and enhancing interpretability. The following are the methods for feature selection in machine learning algorithms that can be used:

Filter Methods: Uses statistical measures to score features independently of the model.

• Correlation (Pearson, Spearman): Select features/variables highly correlated with the target/ dependent variable.
• Variance Threshold: Removes low-variance features.
• Chi-square Test: For categorical target variables.
• Mutual Information: Measures dependency between features and target.

Wrapper Method: Uses model performance to select the best subset of features.

• Forward Selection: Starts with no features, adds one by one.
• Backward Elimination: Starts with all features, removes the least significant.
• Recursive Feature Elimination (RFE): Iteratively removes weakest features.

Embedded Methods: Feature selection is built into the model training process.

• Lasso (L1 Regularization): Penalizes less important features to zero.
• Decision Trees/Random Forest: Feature importance scores based on splits.
• XGBoost/LightGBM: Built-in feature importance metrics.

Dimensionality Reduction: Transforms features into a lower-dimensional space.

• PCA (Principal Component Analysis): Projects data into uncorrelated components.
• t-SNE, UMAP: Non-linear techniques for visualization and feature reduction.

Hybrid Methods: Combines filter and wrapper methods for better efficiency (e.g., feature importance + RFE).

Suppose a dataset consisting of variables having more than 30% missing values? Out of 50 variables, 8 variables have missing values higher than 30%. How will you deal with them?

When dealing with variables that have more than 30% missing values, one needs a systematic approach to decide whether to keep, impute, or drop them.

Step 1: Analyze Missingness Pattern

• Check if missingness is random (MCAR, MAR) or systematic (MNAR).
• Use visualization (e.g., missingno matrix in Python) to detect patterns.

Step 2: Evaluate Variable Importance

• If the variable is critical (a key predictor), consider imputation (but be cautious, as >30% missing data can introduce bias).
• If the variable is unimportant, drop it to avoid noise and complexity.

Step 3: Handling Strategies

• Option 1: Drop the Variables
  • If the variables are not strongly correlated with the target.
  • If domain knowledge suggests they are irrelevant.

The pros of dropping the variables simplify the dataset, avoid imputation bias. The cons of dropping the variables include, potential loss of useful information.

• Option 2: Imputation
  • For numerical variables:
    • Median/Mean imputation (if distribution is not skewed).
    • Predictive imputation (e.g., KNN, regression, or MICE).
  • For categorical variables:
    • Mode imputation (most frequent category).
    • “Missing” as a new category (if missingness is informative).

The pros of imputation retain data, useful if the variable is important. The cons of imputation can distort relationships if missingness is not random.

• Option 3: Flag Missingness & Impute
  • Create a binary flag variable (1 if missing, 0 otherwise).
  • Then impute the missing values (e.g., median/mode).
  • Useful when missingness is informative (e.g., MNAR).

• Option 4: Advanced Techniques
  • Multiple Imputation (MICE): Good for MAR (Missing at Random).
  • Model-based imputation (XGBoost, MissForest).
  • Deep learning methods (e.g., GAIN, Autoencoders).

Step 4: Validate the Impact

• Compare model performance before & after handling missing data.
• Check if the imputation introduces bias.

Note that for feature selection in machine learning:

• Drop if variables are not important (>30% missing is risky for imputation).
• Impute + Flag if the variable is critical and missingness is meaningful.
• Use advanced imputation (MICE, MissForest) if the data is MAR/MNAR.

You got a dataset to work with, having $p$ (number of variables) > $n$ (number of observations). Why is OLS a bad option to work with? Which techniques would be best to use? Why?

When the number of variables ($p$) exceeds the number of observations ($n$), OLS regression fails because:

• No Unique Solution
  • OLS requires solving $(X^TX)^{−1}X^Ty$, but when $p>n$, $X^TX$ is singular (non-invertible).
  • Infinite possible solutions exist, leading to overfitting.
• High Variance & Overfitting: With too many predictors, OLS fits noise rather than true patterns, making predictions unreliable.
• Multicollinearity Issues: Many predictors are often correlated, making coefficient estimates unstable.

In summary, in high-dimensional data sets, one cannot use classical regression techniques, since their assumptions tend to fail. When $p > n$, we can no longer calculate a unique least squares coefficient estimate; the variances become infinite, so OLS cannot be used at all.

Better Techniques for $p>n$ Problems

1. Regularized Regression (Shrinkage Methods): They penalize large coefficients, reducing overfitting.
   • Ridge Regression (L2 Penalty): Shrinks coefficients but never to zero, and works well when many predictors are relevant.
   • Lasso Regression (L1 Penalty): Forces some coefficients to exactly zero, performing feature selection. Lasso regression is best when only a few predictors matter.
   • Elastic Net (L1 + L2 Penalty): Combines Ridge & Lasso advantages. It is useful when there are correlated predictors.
2. Dimension Reduction Techniques: They reduce pp by transforming variables into a lower-dimensional space.
   • Principal Component Regression (PCR): Uses PCA to reduce dimensions before regression.
   • Partial Least Squares (PLS): Like PCR, but considers the target variable in projection.
3. Tree-Based & Ensemble Methods: They handle high-dimensional data well by selecting important features.
   • Random Forest / XGBoost / LightGBM: Automatically perform feature selection and are robust to multicollinearity.
4. Bayesian Methods: Bayesian Ridge Regression uses priors to stabilize coefficient estimates.
5. Sparse Regression Techniques:
   • Stepwise Regression (Forward/Backward): Iteratively selects the best subset of features.
   • Least Angle Regression (LARS): Efficiently handles high-dimensional data.

Which Method is Best for variable selection in machine learning?

Learn about How to Save Data in R Language