Unit 5 - Practice Quiz

CSE274 60 Questions
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1 What is the primary principle of ensemble learning?

Introduction to ensemble learning Easy
A. Combining the predictions of multiple models to produce a better result than any single model.
B. Reducing the number of features in a dataset before training a model.
C. Focusing solely on data preprocessing to improve model accuracy.
D. Using a single, highly complex model to achieve the best performance.

2 Which of the following describes how models are trained in a Bagging ensemble?

Bagging & Boosting Ensembles Easy
A. Independently and in parallel on different random subsets of the training data.
B. Sequentially, where each model is a more complex version of the previous one.
C. On the entire dataset, but with different algorithms.
D. Sequentially, where each new model corrects the errors of the previous one.

3 What is the key difference in the approach between Bagging and Boosting?

Bagging & Boosting Ensembles Easy
A. Bagging uses strong learners, while Boosting uses weak learners.
B. Bagging can only be used for classification, while Boosting is only for regression.
C. Bagging trains models in parallel, while Boosting trains them sequentially.
D. Bagging aims to reduce bias, while Boosting aims to reduce variance.

4 In a 'hard voting' majority classifier with five models, what is the final prediction if their individual predictions for a sample are [A, B, A, C, A]?

majority voting classifier Easy
A. No prediction can be made
B. B
C. C
D. A

5 Random Forest is an ensemble method built upon which base learning algorithm?

Random Forest Easy
A. Linear Regression
B. Support Vector Machines
C. K-Nearest Neighbors
D. Decision Trees

6 Besides bootstrapping the data samples, what is the other main source of randomness in a Random Forest algorithm?

Random Forest Easy
A. It randomly removes data points from the training set.
B. It uses a random number for its final prediction.
C. It randomly selects a subset of features to consider at each split.
D. It randomly assigns weights to the models.

7 What is the main idea behind AdaBoost (Adaptive Boosting)?

AdaBoost Easy
A. To sequentially train models, giving more weight to data points that previous models misclassified.
B. To build a single, very deep decision tree.
C. To average the predictions of many different types of models.
D. To train many models in parallel on random subsets of data.

8 In Gradient Boosting Machines (GBMs), what do the subsequent models in the sequence learn to predict?

Gradient Boosting Machines Easy
A. The residual errors made by the predecessor models.
B. The class probabilities directly.
C. The original target variable.
D. A random value to ensure diversity.

9 What does the 'XG' in XGBoost stand for?

XGBoost Easy
A. Cross-validated Gradient
B. Expanded Gradient
C. Extra Generalization
D. Extreme Gradient

10 Which tree growth strategy is a key feature of LightGBM that makes it particularly fast?

LightGBM Easy
A. Leaf-wise growth
B. Depth-wise growth
C. Level-wise growth
D. Random growth

11 CatBoost is an algorithm specifically designed to handle what type of data very effectively?

CatBoost Easy
A. Unstructured text data
B. Time-series data
C. Categorical features
D. Image data

12 When creating an ensemble of regression models, what is the most common method for combining the predictions from the individual models?

Ensemble Regression Models Easy
A. Selecting the prediction with the highest value.
B. Taking the average or weighted average of the predictions.
C. Taking the mode (most frequent value) of the predictions.
D. Using a majority vote.

13 What is a 'hyperparameter' in the context of a machine learning model?

Model evaluation and hyperparameter tuning Easy
A. The final output or prediction of the model.
B. A parameter learned from the data during training, like the weights in a linear regression.
C. A configuration setting for the model that is set before the training process begins.
D. The performance metric used to evaluate the model, such as accuracy.

14 What is the main benefit of using a Pipeline (e.g., in scikit-learn)?

Pipelines Easy
A. It guarantees that the model will not overfit the data.
B. It bundles preprocessing steps and a model into a single, unified workflow.
C. It significantly reduces the amount of data needed for training.
D. It automatically selects the best machine learning model for a given dataset.

15 What is the primary purpose of cross-validation?

Cross-validation strategies Easy
A. To simplify the model's architecture automatically.
B. To obtain a more reliable estimate of a model's performance on unseen data.
C. To train a model on the entire dataset simultaneously.
D. To speed up the model training process.

16 In 5-Fold Cross-Validation, how many times is a model trained and evaluated?

Cross-validation strategies Easy
A. 1 time
B. 25 times
C. 5 times
D. 10 times

17 How does the Grid Search algorithm work for hyperparameter tuning?

Grid Search Easy
A. It exhaustively trains and evaluates a model for every possible combination of the specified hyperparameter values.
B. It randomly selects combinations of hyperparameter values to test.
C. It starts with default values and adjusts them based on the gradient of the loss function.
D. It uses a probabilistic model to predict which hyperparameters will perform best.

18 What is the main disadvantage of using Grid Search for hyperparameter tuning?

Grid Search Easy
A. It is often too fast to find a good solution.
B. It only works for classification problems.
C. It cannot be used with cross-validation.
D. It can be very slow and computationally expensive, especially with a large number of hyperparameters.

19 What is the primary advantage of Random Search over Grid Search?

Random Search Easy
A. It is guaranteed to find the absolute best combination of hyperparameters.
B. It requires no pre-defined range of hyperparameter values.
C. It always produces a more accurate final model.
D. It is typically much faster and more computationally efficient at finding good hyperparameter combinations.

20 Which of the following best describes the high-level approach of Bayesian Optimization for hyperparameter tuning?

Bayesian Optimization Easy
A. It uses a fixed, predefined schedule to test hyperparameters.
B. It builds a probabilistic model to intelligently choose the next best hyperparameters to evaluate.
C. It tests every possible hyperparameter combination.
D. It tests random hyperparameter combinations.

21 You have a machine learning model that suffers from high bias (underfitting). Which of the following ensemble strategies would be the most appropriate choice to address this specific problem, and why?

Bagging & Boosting Ensembles Medium
A. Stacking, because it uses a meta-learner to combine predictions, which is only effective for low-bias models.
B. Random Forest, because it is a specific implementation of bagging designed to handle high variance.
C. Bagging, because it trains independent models on different subsets of data to reduce variance.
D. Boosting, because it builds models sequentially, with each new model focusing on the errors made by the previous ones.

22 In a Random Forest model, what is the primary effect of decreasing the max_features hyperparameter (the number of features considered for each split)?

Random Forest Medium
A. It increases the diversity of the trees in the forest, which generally helps to reduce the model's overall variance.
B. It decreases the bias of each individual tree, making the overall model more accurate.
C. It increases the correlation between the trees in the forest, leading to higher variance.
D. It reduces the training time significantly with no impact on model performance.

23 In the AdaBoost algorithm, after a weak learner (classifier) makes predictions, the weights of the training samples are updated. If a sample was correctly classified, its weight for the next iteration will:

AdaBoost Medium
A. Remain unchanged, as weights are only updated for misclassified samples.
B. Decrease, so the next learner focuses more on misclassified samples.
C. Increase, to focus more on easy-to-classify samples.
D. Be set to zero, effectively removing it from the training set.

24 What is the primary role of the new decision tree being trained at each iteration of a standard Gradient Boosting Machine (GBM) for a regression task?

Gradient Boosting Machines Medium
A. To predict the residuals (the negative gradient of the loss function) of the preceding ensemble's predictions.
B. To predict the target variable directly using a random subset of the data.
C. To act as a meta-learner that combines the predictions of all previous trees.
D. To model the relationship between features and the classification errors of the previous model.

25 You are using a soft voting ensemble with three probabilistic classifiers for a binary classification problem (Class 0 vs. Class 1). For a new data point, they output the following probabilities for Class 1: Classifier A: 0.8, Classifier B: 0.4, Classifier C: 0.2. What will be the final predicted class?

majority voting classifier Medium
A. The result is a tie and cannot be determined.
B. Class 1
C. Depends on the weights assigned to each classifier.
D. Class 0

26 Beyond the standard Gradient Boosting framework, what is a key feature of XGBoost's objective function that helps it control model complexity and prevent overfitting?

XGBoost Medium
A. It uses a much higher learning rate by default.
B. It fits each new tree to the raw errors instead of the gradient of the loss function.
C. It only allows the use of decision stumps (trees with a depth of 1) as weak learners.
D. It includes built-in L1 (Lasso) and L2 (Ridge) regularization terms on the leaf weights.

27 What is the primary difference in the tree-growth strategy between LightGBM and traditional Gradient Boosting implementations like XGBoost, and what is the main advantage of this difference?

LightGBM Medium
A. LightGBM uses a 'leaf-wise' growth strategy, which is often faster and more memory-efficient.
B. LightGBM grows trees level-wise, which is more accurate but slower.
C. LightGBM only supports linear models as base learners, sacrificing accuracy for speed.
D. LightGBM uses oblique splits instead of axis-parallel splits, improving its handling of correlated features.

28 How does CatBoost primarily handle categorical features, which gives it an advantage over other Gradient Boosting libraries that require manual preprocessing for such features?

CatBoost Medium
A. It uses a combination of frequency encoding and target encoding.
B. It implements an optimized version of 'ordered target statistics' to prevent target leakage.
C. It converts all categorical features to numerical labels based on their alphabetical order.
D. It internally performs one-hot encoding on all categorical features before training.

29 You are building a classification model that requires feature scaling (e.g., StandardScaler) before training. Why is it critical to place the scaler and the model inside a Pipeline when performing cross-validation?

Pipelines Medium
A. To ensure the scaler is only fit once on the entire dataset, saving computational time.
B. To guarantee that the model and scaler use the same random state for reproducibility.
C. To allow for different scaling methods to be used for different features automatically.
D. To prevent data leakage by ensuring the scaler is fit only on the training fold for each cross-validation split.

30 You are tasked with building a model to predict customer churn. The dataset is highly imbalanced, with only 3% of customers churning. Which cross-validation strategy is most appropriate for this scenario to ensure that performance metrics are reliable?

Cross-validation strategies Medium
A. Stratified K-Fold cross-validation, as it preserves the percentage of samples for each class in each fold.
B. TimeSeriesSplit, as it respects the temporal order of customer sign-ups.
C. Leave-One-Out Cross-Validation (LOOCV), as it provides the most thorough evaluation.
D. Standard K-Fold cross-validation, as it randomly shuffles the data.

31 A data scientist is using Grid Search to tune a model with three hyperparameters: learning_rate with 5 possible values, n_estimators with 4 values, and max_depth with 6 values. If they use 5-fold cross-validation, how many times will a model be trained in total?

Grid Search Medium
A. 120
B. 24
C. 15
D. 600

32 When tuning a large number of hyperparameters, what is the primary theoretical advantage of using Random Search over Grid Search, assuming a fixed budget of, for example, 100 trials?

Random Search Medium
A. Random Search is guaranteed to find the global optimum hyperparameter combination.
B. Random Search trains faster for each individual trial compared to a Grid Search trial.
C. Random Search systematically reduces the search space after each trial.
D. Random Search is more efficient because some hyperparameters may have little to no effect on performance, and Grid Search wastes trials exploring them.

33 In Bayesian Optimization for hyperparameter tuning, what are the two main components of the process that are updated iteratively?

Bayesian Optimization Medium
A. A probabilistic surrogate model (e.g., Gaussian Process) and an acquisition function (e.g., Expected Improvement).
B. A neural network for feature extraction and a linear model for prediction.
C. A random search grid and a gradient descent optimizer.
D. A decision tree and a support vector machine.

34 You have created an ensemble of five different regression models (e.g., Linear Regression, Decision Tree Regressor, etc.). How would a standard 'voting regressor' combine their outputs to make a final prediction for a new data point?

Ensemble Regression Models Medium
A. It calculates the median of the individual model predictions.
B. It calculates the average of the individual model predictions.
C. It selects the prediction from the model with the lowest training error.
D. It uses a separate classification model to choose the best prediction.

35 A Gradient Boosting model shows an accuracy of 99.8% on the training set but only 85% on the validation set. This indicates a problem of high variance (overfitting). Which hyperparameter tuning strategy is most likely to mitigate this issue?

Model evaluation and hyperparameter tuning Medium
A. Decreasing the learning_rate and/or decreasing max_depth of the trees.
B. Increasing the learning_rate and increasing n_estimators.
C. Setting min_samples_split to its lowest possible value of 2.
D. Increasing both subsample and max_features to 1.0 to use all data and features.

36 For an ensemble of diverse classifiers to be more accurate than any of its individual members, what is the most critical condition regarding the individual classifiers?

Introduction to ensemble learning Medium
A. All classifiers must be of the same type (e.g., all decision trees).
B. Each classifier must have an accuracy greater than 90%.
C. The classifiers must be better than random guessing, and their errors should be at least somewhat uncorrelated.
D. The classifiers' predictions must be perfectly correlated.

37 During the tree splitting process, how does XGBoost's default behavior for handling missing values differ from libraries that would require imputation beforehand?

XGBoost Medium
A. It learns a default direction (left or right child node) for missing values at each split based on which direction maximizes the gain.
B. It treats missing values as a separate category and creates a dedicated branch for them.
C. It automatically replaces all missing values with the mean of the feature.
D. It drops any rows containing missing values before building each tree.

38 Why is the AdaBoost algorithm particularly sensitive to noisy data and outliers compared to an algorithm like Random Forest?

AdaBoost Medium
A. Because AdaBoost uses deep decision trees that can easily fit to outliers.
B. Because AdaBoost's weight update mechanism will progressively increase the focus on misclassified outliers, potentially distorting the model.
C. Because AdaBoost requires all data to be normalized, which is affected by outliers.
D. Because Random Forest automatically removes outliers during its bootstrap sampling phase.

39 In Bayesian Optimization, what is the role of the 'acquisition function' like Expected Improvement (EI)?

Bayesian Optimization Medium
A. To decide the next set of hyperparameters to evaluate by balancing exploration and exploitation.
B. To serve as the final performance metric for the model.
C. To regularize the model during training to prevent overfitting.
D. To act as a probabilistic surrogate model of the objective function.

40 If you significantly decrease the learning_rate (shrinkage) in a Gradient Boosting model, how should you adjust n_estimators to maintain a similar level of performance, and what is the trade-off?

Gradient Boosting Machines Medium
A. You should increase n_estimators; the trade-off is a longer training time but often a more robust model.
B. The n_estimators parameter should remain unchanged; learning rate does not affect the optimal number of trees.
C. You should increase n_estimators; the trade-off is a higher risk of underfitting.
D. You should also decrease n_estimators; the trade-off is much faster training time.

41 In the standard AdaBoost algorithm for a binary classification task, a weak learner at step achieves a weighted error rate . What is the direct consequence for the weight of this learner, , and how does this impact the subsequent update of sample weights?

AdaBoost Hard
A. becomes negative, causing the sample weight update to effectively increase the weights of correctly classified instances and decrease the weights of misclassified instances.
B. is set to zero, effectively discarding the weak learner, and sample weights remain unchanged for the next iteration.
C. The algorithm terminates immediately, as an error rate > 0.5 indicates failure to learn.
D. becomes negative, but the absolute value is used, so the sample weight update proceeds as if the learner was better than random.

42 In Gradient Boosting Machines (GBM) for regression, each new tree is trained to predict the negative gradient of the loss function with respect to the previous model's predictions. If the loss function is Mean Squared Error (MSE), , what does the negative gradient, , simplify to?

Gradient Boosting Machines Hard
A. The squared residuals, .
B. A constant value determined by the learning rate.
C. The absolute error, .
D. The residuals, .

43 You are using a Random Forest for a regression task with a dataset containing two highly correlated features, X1 and X2, which are both very predictive of the target. After training, you examine the feature importance scores (e.g., Gini importance or permutation importance). What is the most likely outcome for the importance scores of X1 and X2?

Random Forest Hard
A. The total importance will be split between X1 and X2, potentially making both appear less important than a single, moderately useful but uncorrelated feature.
B. The Random Forest algorithm will automatically discard one of the correlated features during the bagging process.
C. One of the features (e.g., X1) will receive a very high importance score, while the other (X2) will receive a score close to zero.
D. Both X1 and X2 will receive high importance scores, accurately reflecting their individual predictive power.

44 The XGBoost objective function includes a regularization term: . What is the primary role of the (gamma) parameter in this equation?

XGBoost
A. It is an L2 regularization parameter on the leaf weights to prevent them from becoming too large.
B. It acts as a complexity control parameter that penalizes the number of terminal nodes (leaves), encouraging pruning.
C. It is an L1 regularization parameter on the leaf weights, encouraging sparsity.
D. It is a learning rate applied specifically to the regularization part of the objective function.

45 LightGBM's default leaf-wise tree growth strategy is generally faster and achieves lower loss than the level-wise growth used by many other boosting algorithms. However, what is its main potential disadvantage, especially on smaller datasets?

LightGBM Hard
A. It is computationally slower than level-wise growth for datasets with few features.
B. It consumes significantly more memory due to its complex data structures for finding the best split.
C. It is more prone to overfitting by focusing on deeply growing one side of the tree before others.
D. It cannot handle categorical features, requiring extensive preprocessing.

46 CatBoost's implementation of Ordered Boosting is specifically designed to combat a problem known as 'prediction shift' or 'target leakage' that can occur when encoding categorical features. How does it achieve this?

CatBoost Hard
A. It uses a separate, independent dataset to pre-calculate all categorical feature encodings before training begins.
B. It applies a large L2 regularization penalty to the encodings of categorical features, shrinking them towards zero.
C. For each sample, it computes the target statistic (e.g., average target value) for a categorical feature using only the samples that appeared before it in a random permutation of the training data.
D. It uses a one-hot encoding scheme for all categorical features, but only for those with cardinality below a certain threshold.

47 In Bayesian Optimization for hyperparameter tuning, the acquisition function (e.g., Expected Improvement, EI) plays a crucial role. What fundamental trade-off is the acquisition function designed to manage?

Bayesian Optimization Hard
A. The exploration-exploitation trade-off: balancing trying new hyperparameters in uncertain regions vs. refining the best-known hyperparameters.
B. The speed-accuracy trade-off: deciding whether to run a quick model evaluation or a more thorough one.
C. The memory-computation trade-off: managing the size of the Gaussian Process model against the cost of updating it.
D. The bias-variance trade-off: ensuring the surrogate model has both low bias and low variance.

48 A researcher wants to tune hyperparameters for a Random Forest and provide an unbiased estimate of its generalization performance. They perform a 10-fold cross-validation where, inside each fold, they use Grid Search with an inner 5-fold cross-validation to find the best hyperparameters. They then train a model with these best parameters on the full 90% training data of the outer fold and evaluate on the 10% test fold. What is this procedure called and why is it necessary?

Cross-validation strategies Hard
A. Leave-One-Out Cross-Validation; it is necessary for small datasets to reduce the variance of the performance estimate.
B. Nested Cross-Validation; it is necessary to prevent the hyperparameter selection from being biased by the same data used for final performance evaluation.
C. Stratified K-Fold Cross-Validation; it is necessary to maintain the class distribution in each fold.
D. Repeated K-Fold Cross-Validation; it is necessary to get a more robust estimate by repeating the CV process multiple times.

49 Consider a scikit-learn Pipeline containing a StandardScaler and a LogisticRegression classifier, which is then evaluated using cross_val_score(pipeline, X, y, cv=5). How does the StandardScaler operate during this process to avoid data leakage?

Pipelines Hard
A. In each of the 5 folds, the StandardScaler is fit only on the training portion of that fold and then used to transform both the training and validation portions of that fold.
B. The StandardScaler is fit on the entire dataset X once before the cross-validation process begins.
C. The StandardScaler is refit from scratch on the validation data within each fold before predictions are made.
D. A separate StandardScaler is fit for each class in the data, and the appropriate one is applied based on the predicted class.

50 The variance of an ensemble of M regression models is given by , where is the variance of each individual model and is the average pairwise correlation of their errors. If you have an ensemble of 100 highly-correlated models (), what is the approximate variance of the ensemble compared to a single model's variance ?

Ensemble Regression Models Hard
A. Approximately ; the variance is reduced by a factor of 100, regardless of correlation.
B. Approximately ; there is no variance reduction at all.
C. Approximately ; the variance is reduced by a factor of 10.
D. Approximately ; the variance reduction is minimal because the models make similar errors.

51 Consider a hard voting ensemble of three independent binary classifiers. Classifier 1 has an accuracy of 0.8, Classifier 2 has an accuracy of 0.8, and Classifier 3 has an accuracy of 0.6. Assuming their errors are uncorrelated, what is the probability that the ensemble makes a correct prediction?

majority voting classifier Hard
A. 0.733
B. 0.924
C. 0.800
D. 0.896

52 When tuning a model with two hyperparameters, one with 10 possible values and another with 20, a GridSearchCV with 5-fold cross-validation is performed. A competing RandomizedSearchCV is set to run for 40 iterations with 5-fold cross-validation. Which of the following statements is a correct analysis of the computational cost?

Grid Search Hard
A. GridSearchCV will train models, while RandomizedSearchCV will train models, offering a significant speed-up at the cost of not exploring all combinations.
B. GridSearchCV will train models, and RandomizedSearchCV will train $40$ models.
C. GridSearchCV will train models, while RandomizedSearchCV will train models.
D. Both methods will train the same number of models, but GridSearchCV is more systematic.

53 How does increasing the max_features parameter in a Random Forest typically affect the model's bias and variance?

Random Forest Hard
A. It decreases the correlation between the trees, which decreases the ensemble's variance, and also decreases the individual tree's bias.
B. It has no significant effect on bias but decreases the variance by a factor related to the square root of max_features.
C. It increases the correlation between the trees, which increases the ensemble's variance, and also increases the individual tree's bias.
D. It increases the correlation between the trees, which increases the ensemble's variance, but decreases the individual tree's bias.

54 How does XGBoost's default behavior for handling missing values work during the tree building process?

XGBoost Hard
A. It treats missing values as a separate category and creates a third branch at each split specifically for them.
B. It imputes all missing values with the mean/median of the feature before training begins.
C. It drops all rows containing any missing values.
D. At each split, it learns a default direction (left or right) for missing values by sending all instances with missing values down each path and choosing the one that maximizes the gain.

55 Which of the following statements best synthesizes the primary difference in how Bagging and Boosting ensembles approach the bias-variance trade-off?

Bagging & Boosting Ensembles Hard
A. Bagging primarily reduces variance by averaging low-bias, high-variance models, while Boosting primarily reduces bias by sequentially training weak learners on the mistakes of their predecessors.
B. Both Bagging and Boosting primarily reduce variance, but Boosting does so more effectively by using a weighted average.
C. Bagging primarily reduces bias by creating diverse models from bootstrapped samples, while Boosting primarily reduces variance by giving higher weights to misclassified samples.
D. Both Bagging and Boosting primarily reduce bias, but Bagging is more robust to outliers.

56 LightGBM's Gradient-based One-Side Sampling (GOSS) is an efficiency optimization. What is the main assumption behind GOSS that allows it to safely downsample the data instances for finding the best split?

LightGBM Hard
A. Instances with large gradients are likely outliers and should be downsampled to create a more robust model.
B. Data instances can be clustered, and only the cluster centroids are needed to calculate information gain.
C. All instances contribute equally to the information gain, so random sampling is sufficient.
D. Instances with small gradients have already been well-trained and contribute little to the information gain, so they can be mostly ignored.

57 Besides its Ordered Boosting for categorical features, CatBoost also uses 'oblivious decision trees' as base learners. What is a key characteristic of an oblivious decision tree and what is its main advantage?

CatBoost Hard
A. It can only have a maximum depth of one (a stump), forcing it to be a very weak learner.
B. It is 'oblivious' to the target variable and splits only based on feature distributions, a technique for unsupervised learning.
C. It is a tree structure where each node can have more than two children, allowing for more complex splits.
D. It uses the same splitting criterion (feature and threshold) across an entire level of the tree; this makes the model less prone to overfitting and extremely fast for prediction.

58 When analyzing cross-validation results to select the best hyperparameter (e.g., C in an SVM), the 'one standard error' rule is often used. What is the main purpose of this rule?

Model evaluation and hyperparameter tuning Hard
A. To select the simplest model whose performance is statistically comparable to the best-performing model, favoring regularization to prevent overfitting.
B. To add one standard error to the mean performance score as a bonus for model complexity.
C. To discard any model whose performance standard deviation across folds is greater than one.
D. To select the model with the absolute highest mean performance score across all folds, regardless of variance.

59 AdaBoost can be interpreted as a forward stagewise additive model that minimizes the exponential loss function, . What property of this loss function makes AdaBoost particularly sensitive to outliers?

AdaBoost Hard
A. It uses the hinge loss, which has a linear penalty for misclassified points.
B. It assigns an exponentially increasing penalty to misclassified points with high confidence, forcing the model to focus heavily on them.
C. It is a convex loss function, which guarantees a global minimum but is slow to converge.
D. It is non-differentiable, requiring the use of sub-gradient descent methods.

60 The 'No Free Lunch' theorem is a fundamental concept in machine learning. How does it apply to the choice of ensemble learning models?

Introduction to ensemble learning Hard
A. It states that the computational cost of an ensemble is always proportional to its performance gain.
B. It implies that no single ensemble model (e.g., Random Forest, XGBoost) is guaranteed to be the best-performing model for all possible datasets.
C. It proves that ensemble models are always superior to single models across all datasets.
D. It guarantees that for every dataset, there exists an ensemble model that can achieve perfect accuracy.