Multiple Choice Questions PDF

**Which statistical measure is commonly used to evaluate the explanatory power of a model by quantifying the strength of the relationship between variables?**\ A) Mean Squared Error (MSE)\ B) R2**\ **C) Pearson\'s correlation coefficient\ D) Akaike Information Criterion (AIC)\ E) None of the above...

**Which statistical measure is commonly used to evaluate the explanatory power of a model by quantifying the strength of the relationship between variables?**\ A) Mean Squared Error (MSE)\ B) R2**\ **C) Pearson\'s correlation coefficient\ D) Akaike Information Criterion (AIC)\ E) None of the above 2. **In the context of predictive modeling, which method is often used to assess the accuracy of predictions?** A\) Root Mean Square Error (RMSE)\ B) Chi-squared test\ C) F-test\ D) T-test\ E) None of the above 3. **Which statement best describes the relationship between explanatory power and predictive power in statistical models?\ **A) Explanatory power guarantees high predictive power.\ B) Predictive power is not influenced by explanatory power.\ C) Explanatory models always have better predictive accuracy than predictive models.\ D) Explanatory power does not imply predictive power.**\ **E) None of the above 4. **What is the primary goal of an explanatory statistical model?\ **A) To forecast future observations based solely on past data.\ B) To minimize the mean squared error between predicted and observed values.\ C) To understand and explain the causal mechanisms behind observed phenomena.**\ **D) To maximize the predictive accuracy on new, unseen data.\ E) None of the above 5. **In the context of machine learning, what is an equivalent term for \"Feature\"?** A\) Dependent Variable\ B) Independent Variable\ C) Label\ D) Dataset\ E) None of the above 6. **What does a \"labelled dataset\" primarily contain?** A\) Only independent variables\ B) Parameters for model fitting\ C) Observations without any response variable\ D) Data on the label/target and predictor variables**\ **E) None of the above 7. **What process is referred to as \"Training\" in machine learning**\ A) Data collection\ B) Data cleaning\ C) Model fitting, i.e. parameter estimation**\ **D) Prediction making\ E) None of the above 8. **Which of the following is not one of the three distinct types of machine learning?** A\) Supervised Learning\ B) Unsupervised Learning\ C) Semi-supervised Learning**\ **D) Reinforcement Learning\ E) None of the above 9. **What are the two main tasks in supervised learning?** 10. **What distinguishes a classification task in supervised learning?** **Which of the following best describes \"Classification\" in the context of supervised learning?** 14. **Which of the following statements is not true about the classification task of supervised learning?** A\) Binary Classification is used when there are only two possible classes. 15. **What is \"Regression\" in supervised learning primarily concerned with?** 16. **How does clustering differ from supervised learning?** 17. **Which of the following is a practical application of clustering?** 1. **What is the term used for the estimated coefficients in the context of the algorithm discussed?** 2. **What does the decision function output if the net input is greater than or equal to zero?** 3. **For the perceptron to converge, the two classes must be:** 4. **In multi-class classification, how is the perceptron algorithm extended?** 5. **What is the role of the bias unit in linear classification models?** 6. **What does the ADALINE algorithm learn to minimize?** 7. **What can a perceptron be viewed as?** 8. **What is the key difference between the Adaline rule and the Perceptron?** 9. **What is the main advantage of using a linear activation function in Adaline compared to the unit step function?** 10. **What does the objective function in Adaline represent?** 11. **What method is used in Adaline to find the optimal weights?** 12. **What does feature standardization typically lead to in optimization algorithms?** 13. **What is an epoch in machine learning?** 14. **What is stochastic gradient descent (SGD) particularly useful for?** 15. **Which of the following is a key characteristic of mini-batch gradient descent?** 16. **Which of the following options is not true about the learning rate?** 1. **What is the main objective of Support Vector Machines (SVMs) in the context of classification problems?** 2. **What term is used to describe the observations that are closest to the decision boundary in SVMs?** 3. **Which of the following statements is not true about the characteristics of SVMs?** 4. **In the context of SVMs, what is the margin?** 5. **How do linear SVMs deal with nonlinearly separable data?** 6. **What is the effect of a large value of C in SVM\'s soft-margin classification?** 7. **Which one of the following is not true regarding SVM\'s strategy for finding the maximum margin?** 8. **Which of the following is not true about kernel methods?** 9. **What is the purpose of employing a nonlinear function in kernel methods?** 10. **What is the implication of making data linearly separable in a higher dimensional space using kernel methods?** 11. **What effect does increasing have in the Radial Basis Function (RBF) kernel?** 12. **Which one is not a characteristic of kernel functions used in practice?** 13. **What does a decision tree use to infer the class labels of examples in a dataset?** 14. **What can result from a very deep decision tree with many nodes?** 15. **How do decision trees typically prevent overfitting?** 16. **Which one is not true about maximising Information Gain (IG) in decision trees?** 17. **What is a characteristic of Random Forests?** 18. **In Random Forests, what does the \"bootstrap sample\" refer to?** 19. **Which of the following is not a hyperparameter in Random Forests?** 20. **What does the KNN algorithm primarily use to classify a new data point?** 21. **The KNN algorithm is considered a \"lazy learner\" because:** 22. **Which one of the following is not a step in the process of training a KNN algorithm?** 23. **What is crucial for finding a good balance between overfitting and underfitting in the KNN algorithm?** 24. **Which one of the following is NOT true regarding the hyperparameters in the KNN algorithm?** 25. **What is an advantage of the KNN algorithm?** 1. **Which method is not a recommended way to impute missing values in a small dataset?** 2. **Which of these is an example of ordinal data?** 3. **Why shouldn't nominal features be encoded using integer values that represent an ordering?** 4. **Which machine learning algorithms don\'t require feature scaling?** 5. **What is the primary purpose of standardisation in feature scaling?** 6. **What can cause overfitting in a model?** 7. **Which technique is not a possible solution for overfitting?** 8. **What does L1 regularisation use to penalise large parameter values?** 9. **What is a drawback of using feature importance in random forests when features are highly correlated?** 10. **What is not a main technique of dimensionality reduction?** 11. **What is the main disadvantage of deleting missing data?** 12. **Which of the following is NOT true about nominal data?** 13. **Why is One-Hot Encoding used for nominal features in machine learning models?** 14. **What is the primary reason for scaling features before using them in machine learning algorithms?** 15. **What is Sequential Backward Selection (SBS) primarily used for in machine learning?** 1. **What is the primary goal of dimensionality reduction?** 2. **Which of the following is NOT a method of dimensionality reduction?** 3. **Which technique is used to find orthogonal features that explain most of the variance in the data?** 4. **Which of the following is true?** 5. **What is the first step in computing Principal Components?** 6. **What is the role of eigenvalues and eigenvectors in PCA?** 7. **How does LDA differ from PCA?** 8. **What is the aim of Linear Discriminant Analysis (LDA)?** 9. **Kernel PCA is used for:** 10. **What does regularisation in the context of dimensionality reduction aim to achieve?** 11. **Which dimensionality reduction technique is described as unsupervised?** 12. **Why is dimensionality reduction needed?** 13. **Which of the following is a consequence of high dimensionality?** 14. **Sequential Feature Selection is an example of:** 1. **What are pipelines in scikit-learn used for?** 2. **Which of the following is a primary advantage of using pipelines in scikit-learn?** 3. **How do pipelines improve the reproducibility of machine learning workflows?** 4. **What is the role of the fit method in a scikit-learn pipeline?** 5. **What does the predict method of a pipeline do?** 6. **What does the holdout method in machine learning involve?** 7. **K-fold cross-validation improves upon the holdout method by:** 8. **The need for cross-validation arises due to:** 9. **What is the purpose of hyperparameter tuning via grid search?** 10. **What do learning curves plot to diagnose model performance issues?** 11. **What can validation curves help identify in a model?** 12. **Which of the following is a component of a confusion matrix?** 13. **Precision in the context of classification models is:** 14. **The F1 score is used to:** 15. **Recall is calculated as:** 1. **What does an ensemble method involve in machine learning?** 2. **Ensemble methods are designed to:** 3. **What is majority voting in the context of ensemble methods?** 4. **Plurality voting is used when:** 5. **Hard voting in predictive ensembles is characterised by:** 6. **Soft voting differs from hard voting by:** 7. **Bagging is an ensemble method that:** 8. **A weak learner is a model that:** 9. **Adaptive Boosting (AdaBoost) works by:** 10. **In AdaBoost, the weights of instances are updated to:** 11. **AdaBoost is sensitive to noisy data and outliers because:** 12. **The main difference between Bagging and AdaBoost is that Bagging**: 13. **AdaBoost updates the instance weights by:** 14. **What is the role of weights in soft voting?** 15. **Which statement is NOT true about majority voting in ensemble methods?** 16. **Which of the following is NOT true about soft voting?** 17. **Which statement is NOT true regarding AdaBoost?** 1. **Which of the following best defines a centroid in clustering?** 2. **A medoid is:** 3. **What is the main advantage of K-Means++ over K-Means?** 4. **The K-Means algorithm is not suited for:** 5. **Euclidean distance is used to:** 6. **The Elbow method is used to determine:** 7. **Silhouette plots assess:** 8. **Hierarchical clustering does not include:** 9. **The complete linkage method:** 10. **A dendrogram is used in:** 11. **DBSCAN algorithm classifies clusters based on:** 12. **Which is not a key concept of DBSCAN?** 13. **What does the initial step in K-Means clustering involve?** 14. **Which of the following is not a goal of unsupervised learning?** 15. **In the context of Euclidean distance, what is d(*y*,*x*) if *y* and *x* are vectors?** 16. **What distinguishes the Elbow method in cluster analysis?** 17. **Which statement about DBSCAN is false?** **1. Which of the following is not true?** **2.What of the following techniques is used in Exploratory Data Analysis (EDA)?** **3. What do scatter plots primarily show?** **4.What does a correlation coefficient of 0 indicate?** **5. What advantage does the correlation matrix offer in multiple linear regression analysis?** **6. Why is the RANSAC algorithm preferred in the presence of data anomalies?** **7. Which method is not used to evaluate the performance of linear regression models?** **8. What does Lasso Regression do differently from Ridge Regression?** **9. Which is a potential disadvantage of polynomial regression models?** **10: What is an advantage of Random Forest Regression over single Decision Tree Regression?** **11 Which regression method includes both L1 and L2 regularisation techniques?** **12.In the context of regression models, what does RANSAC stand for?** **13. Which metric is specifically used to assess the goodness of fit in regression models?** **14. Which of the following is true about Random Forest and Gradient Boosting ensemble methods?** 1. Random Forest is used for classification whereas Gradient Boosting is used for regression problems 2. Random Forest is used for regression problems whereas Gradient Boosting is used for Classification problems 3. Both methods can be only used for classification problems 4. Both methods can be only used for regression problems 5. None of the above **15. For three different regression models based on the same data, the following results were obtained:** **Which model exhibits the overfitting problem?** 1. Model-3 2. Model-2 3. Model-1 4. None of the models 5. All of the models **1. What is Sentiment Analysis primarily used for in the context of machine learning?** **3. What does the Bag-of-Words model ignore when processing text?** **4. Which is not a core component of the Bag-of-Words model?** **5. What does TF-IDF stand for?** **6. What is an N-gram model used to represent?** **7. In Python, what are regular expressions used for?** **8. What is a token in NLP?** **9. What does Word Stemming achieve?** **10. What is the main difference between Count Vectorizer and Hashing Vectorizer?** **11. What is Topic Modeling used for?** **12. Which technique is known as out-of-core learning?** **13. What is primarily reduced by the TF-IDF calculation?** **14. Which of the following is NOT true about the Bag-of-Words model?**