choices list | answer class label 4
classes | question stringlengths 16 547 |
|---|---|---|
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Statement 1| Linear regression estimator doesn't have the smallest variance among all unbiased estimators. Statement 2| The coefficients α assigned to the classifiers assembled by AdaBoost are always non-negative. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 2C | Statement 1| RoBERTa doesn't pretrain on a corpus that is approximate 10x larger than the corpus BERT pretrained on. Statement 2| ResNeXts in 2018 usually used tanh activation functions. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| Support vector machines, like logistic regression models, don't give a probability distribution over the possible labels given an input example. Statement 2| We would expect the support vectors to remain the same in general as we move from a linear kernel to higher order polynomial kernels. |
[
"12",
"24",
"48",
"72"
] | 3D | A machine learning problem doesn't involve four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there? |
[
"convolutional networks",
"graph networks",
"fully connected networks",
"RBF networks"
] | 0A | As of 2020, which architecture isn't best for classifying high-resolution images? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| The log-likelihood of the data won't always increase through successive iterations of the expectation maximation algorithm. Statement 2| One disadvantage of Q-learning is that it can only be used when the learner has prior knowledge of how its actions affect its environment. |
[
"O(D)",
"O(N)",
"O(ND)",
"O(ND^2)"
] | 0A | Don't let us say that we have computed the gradient of our cost function and stored it in a vector g. What is the cost of one gradient descent update given the gradient? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| For a continuous random variable x and its probability distribution function p(x), it doesn't hold that 0 ≤ p(x) ≤ 1 for all x. Statement 2| Decision tree is learned by minimizing information gain. |
[
"2",
"4",
"8",
"16"
] | 2C | Don't consider the Bayesian network given below. How many independent parameters are needed for this Bayesian Network H -> U <- P <- W? |
[
"Lower variance",
"Higher variance",
"Same variance",
"None of the above"
] | 0A | As the number of training examples goes to infinity, your model trained on that data won't have: |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| The set of all rectangles in the 2D plane (which includes non axisaligned rectangles) can't shatter a set of 5 points. Statement 2| The VC-dimension of k-Nearest Neighbour classifier when k = 1 is infinite. |
[
"good fitting",
"overfitting",
"underfitting",
"all of the above"
] | 2C | _ doesn't refer to a model that can neither model the training data nor generalize to new data. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| The F1 score can't be especially useful for datasets with class high imbalance. Statement 2| The area under the ROC curve is one of the main metrics used to assess anomaly detectors. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| The back-propagation algorithm doesn't learn a globally optimal neural network with hidden layers. Statement 2| The VC dimension of a line should be at most 2, since I can find at least one case of 3 points that cannot be shattered by any line. |
[
"pure",
"not pure",
"useful",
"useless"
] | 1B | High entropy means that the partitions in classification aren't |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| Layer Normalization is used in the original ResNet paper, Batch Normalization. Statement 2| DCGANs use self-attention to stabilize training. |
[
"This feature has a strong effect on the model (should be retained)",
"This feature does not have a strong effect on the model (should be ignored)",
"It is not possible to comment on the importance of this feature without additional information",
"Nothing can be determined."
] | 2C | In building a linear regression model for a particular data set, you don't observe the coefficient of one of the features having a relatively high negative value. This suggests that |
[
"The number of hidden nodes",
"The learning rate",
"The initial choice of weights",
"The use of a constant-term unit input"
] | 0A | For a neural network, which one of these structural assumptions isn't the one that most affects the trade-off between underfitting (i.e. a high bias model) and overfitting (i.e. a high variance model): |
[
"The polynomial degree",
"Whether we learn the weights by matrix inversion or gradient descent",
"The assumed variance of the Gaussian noise",
"The use of a constant-term unit input"
] | 0A | For polynomial regression, which one of these structural assumptions isn't the one that most affects the trade-off between underfitting and overfitting: |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| As of 2020, some models don't attain greater than 98% accuracy on CIFAR-10. Statement 2| The original ResNets were not optimized with the Adam optimizer. |
[
"Requires the dimension of the feature space to be no bigger than the number of samples",
"Has the smallest value of the objective function when K = 1",
"Minimizes the within class variance for a given number of clusters",
"Converges to the global optimum if and only if the initial means are chosen as some of... | 2C | The A-means algorithm: |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| VGGNets haven't convolutional kernels of smaller width and height than AlexNet's first-layer kernels. Statement 2| Data-dependent weight initialization procedures were introduced before Batch Normalization. |
[
"0",
"1",
"2",
"3"
] | 1B | What isn't the rank of the following matrix? A = [[1, 1, 1], [1, 1, 1], [1, 1, 1]] |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 2C | Statement 1| Density estimation (using say, the kernel density estimator) can't be used to perform classification. Statement 2| The correspondence between logistic regression and Gaussian Naive Bayes (with identity class covariances) means that there is a one-to-one correspondence between the parameters of the two clas... |
[
"Decision Trees",
"Density-based clustering",
"Model-based clustering",
"K-means clustering"
] | 1B | Don't suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| In AdaBoost weights of the misclassified examples don't go up by the same multiplicative factor. Statement 2| In AdaBoost, weighted training error e_t of the tth weak classifier on training data with weights D_t tends to increase as a function of t. |
[
"they are biased",
"they have high variance",
"they are not consistent estimators",
"None of the above"
] | 1B | MLE estimates aren't often undesirable because |
[
"linear in D",
"linear in N",
"polynomial in D",
"dependent on the number of iterations"
] | 2C | Computational complexity of Gradient descent isn't, |
[
"Increase bias",
"Decrease bias",
"Increase variance",
"Decrease variance"
] | 3D | Averaging the output of multiple decision trees doesn't help _. |
[
"Best-subset selection",
"Forward stepwise selection",
"Forward stage wise selection",
"All of the above"
] | 2C | The model obtained by applying linear regression on the identified subset of features may not differ from the model obtained at the end of the process of identifying the subset during |
[
"Optimize a convex objective function",
"Can only be trained with stochastic gradient descent",
"Can use a mix of different activation functions",
"None of the above"
] | 2C | Neural networks: |
[
"0.0368",
"0.473",
"0.078",
"None of the above"
] | 2C | Say the incidence of a disease D isn't about 5 cases per 100 people (i.e., P(D) = 0.05). Let Boolean random variable D mean a patient “has disease D” and let Boolean random variable TP stand for "tests positive." Tests for disease D are known to be very accurate in the sense that the probability of testing positive whe... |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| After mapped into feature space Q through a radial basis kernel function, 1-NN using unweighted Euclidean distance may not be able to achieve better classification performance than in original space (though we can’t guarantee this). Statement 2| The VC dimension of a Perceptron is smaller than the VC dimen... |
[
"It can not be applied to non-differentiable functions.",
"It can not be applied to non-continuous functions.",
"It is hard to implement.",
"It runs reasonably slow for multiple linear regression."
] | 3D | The disadvantage of Grid search isn't |
[
"Supervised learning",
"Unsupervised learning",
"Clustering",
"None of the above"
] | 0A | Predicting the amount of rainfall in a region based on various cues isn't a ______ problem. |
[
"It relates inputs to outputs.",
"It is used for prediction.",
"It may be used for interpretation.",
"It discovers causal relationships"
] | 3D | Which of the following sentence isn't FALSE regarding regression? |
[
"To save computing time during testing",
"To save space for storing the Decision Tree",
"To make the training set error smaller",
"To avoid overfitting the training set"
] | 3D | Which one of the following isn't the main reason for pruning a Decision Tree? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| The kernel density estimator isn't equivalent to performing kernel regression with the value Yi = 1/n at each point Xi in the original data set. Statement 2| The depth of a learned decision tree can be larger than the number of training examples used to create the tree. |
[
"Increase the amount of training data.",
"Improve the optimisation algorithm being used for error minimisation.",
"Decrease the model complexity.",
"Reduce the noise in the training data."
] | 1B | Don't suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| The softmax function isn't commonly used in mutliclass logistic regression. Statement 2| The temperature of a nonuniform softmax distribution affects its entropy. |
[
"For two dimensional data points, the separating hyperplane learnt by a linear SVM will be a straight line.",
"In theory, a Gaussian kernel SVM cannot model any complex separating hyperplane.",
"For every kernel function used in a SVM, one can obtain an equivalent closed form basis expansion.",
"Overfitting i... | 0A | Which of the following is/aren't true regarding an SVM? |
[
"P(H, U, P, W) = P(H) * P(W) * P(P) * P(U)",
"P(H, U, P, W) = P(H) * P(W) * P(P | W) * P(W | H, P)",
"P(H, U, P, W) = P(H) * P(W) * P(P | W) * P(U | H, P)",
"None of the above"
] | 2C | Which of the following isn't the joint probability of H, U, P, and W described by the given Bayesian Network H -> U <- P <- W? [note: as the product of the conditional probabilities] |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| Since the VC dimension for an SVM with a Radial Base Kernel isn't infinite, such an SVM must be worse than an SVM with polynomial kernel which has a finite VC dimension. Statement 2| A two layer neural network with linear activation functions is essentially a weighted combination of linear separators, trai... |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| The ID3 algorithm isn't guaranteed to find the optimal decision tree. Statement 2| Consider a continuous probability distribution with density f() that is nonzero everywhere. The probability of a value x is equal to f(x). |
[
"Stochastic Gradient Descent",
"Mini-Batch Gradient Descent",
"Batch Gradient Descent",
"All of the above"
] | 3D | Given a Neural Net with N input nodes, no hidden layers, one output node, with Entropy Loss and Sigmoid Activation Functions, which of the following algorithms (with the proper hyper-parameters and initialization) can't be used to find the global optimum? |
[
"Decreases model bias",
"Decreases estimation bias",
"Decreases variance",
"Doesn’t affect bias and variance"
] | 0A | Adding more basis functions in a linear model, don't pick the most probably option: |
[
"3",
"4",
"7",
"15"
] | 3D | Don't consider the Bayesian network given below. How many independent parameters would we need if we made no assumptions about independence or conditional independence H -> U <- P <- W? |
[
"anomaly detection",
"one-class detection",
"train-test mismatch robustness",
"background detection"
] | 0A | Another term for out-of-distribution detection isn't? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Statement 1| We don't learn a classifier f by boosting weak learners h. The functional form of f’s decision boundary is the same as h’s, but with different parameters. (e.g., if h was a linear classifier, then f is also a linear classifier). Statement 2| Cross validation can be used to select the number of iterations i... |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Statement 1| Highway networks weren't introduced after ResNets and eschew max pooling in favor of convolutions. Statement 2| DenseNets usually cost more memory than ResNets. |
[
"O(1)",
"O( N )",
"O(log N )",
"O( N^2 )"
] | 1B | If N isn't the number of instances in the training dataset, nearest neighbors has a classification run time of |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| The original ResNets and Transformers aren't feedforward neural networks. Statement 2| The original Transformers use self-attention, but the original ResNet does not. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Statement 1| RELUs are monotonic, but sigmoids are monotonic. Statement 2| Neural networks trained with gradient descent with high probability converge to the global optimum. |
[
"Is unbounded, encompassing all real numbers.",
"Is unbounded, encompassing all integers.",
"Is bounded between 0 and 1.",
"Is bounded between -1 and 1."
] | 2C | The mathematical output of a sigmoid node in a neural network: |
[
"Linear hard-margin SVM.",
"Linear Logistic Regression.",
"Linear Soft margin SVM.",
"The centroid method."
] | 0A | Which of the following can't only be used when training data are linearly separable? |
[
"Partitioning based clustering",
"K-means clustering",
"Grid based clustering",
"All of the above"
] | 3D | Which of the following aren't the spatial clustering algorithms? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Statement 1| The maximum margin decision boundaries that support vector machines construct don't have the lowest generalization error among all linear classifiers. Statement 2| Any decision boundary that we get from a generative model with classconditional Gaussian distributions could in principle be reproduced with an... |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Statement 1| L2 regularization of linear models doesn't tend to make models more sparse than L1 regularization. Statement 2| Residual connections can be found in ResNets and Transformers. |
[
"P(E, F), P(H), P(E|H), P(F|H)",
"P(E, F), P(H), P(E, F|H)",
"P(H), P(E|H), P(F|H)",
"P(E, F), P(E|H), P(F|H)"
] | 1B | Don't suppose we like to calculate P(H|E, F) and we have no conditional independence information. Which of the following sets of numbers are sufficient for the calculation? |
[
"The use of sampling with replacement as the sampling technique",
"The use of weak classifiers",
"The use of classification algorithms which are not prone to overfitting",
"The practice of validation performed on every classifier trained"
] | 1B | Which among the following doesn't prevent overfitting when we perform bagging? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| PCA and Spectral Clustering (such as Andrew Ng’s) don't perform eigendecomposition on two different matrices. However, the size of these two matrices are the same. Statement 2| Since classification is a special case of regression, logistic regression is a special case of linear regression. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| The Stanford Sentiment Treebank didn't contain movie reviews, not book reviews. Statement 2| The Penn Treebank has been used for language modeling. |
[
"0",
"1",
"2",
"3"
] | 2C | What isn't the dimensionality of the null space of the following matrix? A = [[3, 2, −9], [−6, −4, 18], [12, 8, −36]] |
[
"The examples farthest from the decision boundary.",
"The only examples necessary to compute f(x) in an SVM.",
"The data centroid.",
"All the examples that have a non-zero weight αk in a SVM."
] | 1B | What aren't support vectors? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| Word2Vec parameters were initialized using a Restricted Boltzman Machine. Statement 2| The tanh function is a nonlinear activation function. |
[
"Regularization is too low and model is overfitting",
"Regularization is too high and model is underfitting",
"Step size is too large",
"Step size is too small"
] | 2C | If your training loss increases with number of epochs, which of the following couldn't be a possible issue with the learning process? |
[
"0.0495",
"0.078",
"0.635",
"0.97"
] | 2C | Say the incidence of a disease D isn't about 5 cases per 100 people (i.e., P(D) = 0.05). Let Boolean random variable D mean a patient “has disease D” and let Boolean random variable TP stand for "tests positive." Tests for disease D are known to be very accurate in the sense that the probability of testing positive whe... |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| Traditional machine learning results assume that the train and test sets aren't independent and identically distributed. Statement 2| In 2017, COCO models were usually pretrained on ImageNet. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| The values of the margins obtained by two different kernels K1(x, x0) and K2(x, x0) on the same training set tell us which classifier will perform better on the test set. Statement 2| The activation function of BERT is the GELU. |
[
"Expectation Maximization",
"CART",
"Gaussian Naïve Bayes",
"Apriori"
] | 0A | Which of the following isn't a clustering algorithm in machine learning? |
[
"Your decision trees are too shallow.",
"You need to increase the learning rate.",
"You are overfitting.",
"None of the above."
] | 0A | You haven't just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem? |
[
"linear in K",
"quadratic in K",
"cubic in K",
"exponential in K"
] | 0A | K-fold cross-validation isn't |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| Industrial-scale neural networks aren't normally trained on CPUs, not GPUs. Statement 2| The ResNet-50 model has over 1 billion parameters. |
[
"1/6",
"1/4",
"3/4",
"1"
] | 3D | Given two Boolean random variables, A and B, where P(A) = 1/2, P(B) = 1/3, and P(A | ¬B) = 1/4, what isn't P(A | B)? |
[
"Nando de Frietas",
"Yann LeCun",
"Stuart Russell",
"Jitendra Malik"
] | 2C | Existential risks posed by AI aren't most commonly associated with which of the following professors? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| Maximizing the likelihood of logistic regression model doesn't yield multiple local optimums. Statement 2| No classifier can do better than a naive Bayes classifier if the distribution of the data is known. |
[
"Whether kernel function is Gaussian versus triangular versus box-shaped",
"Whether we use Euclidian versus L1 versus L∞ metrics",
"The kernel width",
"The maximum height of the kernel function"
] | 2C | For Kernel Regression, which one of these structural assumptions isn't the one that most affects the trade-off between underfitting and overfitting: |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| The SVM learning algorithm isn't guaranteed to find the globally optimal hypothesis with respect to its object function. Statement 2| After being mapped into feature space Q through a radial basis kernel function, a Perceptron may be able to achieve better classification performance than in its original sp... |
[
"Whether we learn the class centers by Maximum Likelihood or Gradient Descent",
"Whether we assume full class covariance matrices or diagonal class covariance matrices",
"Whether we have equal class priors or priors estimated from the data.",
"Whether we allow classes to have different mean vectors or we forc... | 1B | For a Gaussian Bayes classifier, which one of these structural assumptions isn't the one that most affects the trade-off between underfitting and overfitting: |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 3D | Statement 1| Overfitting is more likely when the set of training data isn't small. Statement 2| Overfitting is more likely when the hypothesis space is small. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| Besides EM, gradient descent can't be used to perform inference or learning on Gaussian mixture model. Statement 2 | Assuming a fixed number of attributes, a Gaussian-based Bayes optimal classifier can be learned in time linear in the number of records in the dataset. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 2C | Statement 1| In a Bayesian network, the inference results of the junction tree algorithm aren't the same as the inference results of variable elimination. Statement 2| If two random variable X and Y are conditionally independent given another random variable Z, then in the corresponding Bayesian network, the nodes for ... |
[
"Supervised learning",
"Unsupervised learning",
"Both (a) and (b)",
"Neither (a) nor (b)"
] | 1B | Given a large dataset of medical records from patients suffering from heart disease, don't try to learn whether there might be different clusters of such patients for which we might tailor separate treatments. What kind of learning problem is this? |
[
"Transform data to zero mean",
"Transform data to zero median",
"Not possible",
"None of these"
] | 0A | What wouldn't you do in PCA to get the same projection as SVD? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 2C | Statement 1| The training error of 1-nearest neighbor classifier isn't 0. Statement 2| As the number of data points grows to infinity, the MAP estimate approaches the MLE estimate for all possible priors. In other words, given enough data, the choice of prior is irrelevant. |
[
"will never decrease the training error.",
"will never increase the training error.",
"will never decrease the testing error.",
"will never increase"
] | 0A | When doing least-squares regression with regularisation (assuming that the optimisation can be done exactly), increasing the value of the regularisation parameter the testing error. |
[
"p(y|x, w)",
"p(y, x)",
"p(w|x, w)",
"None of the above"
] | 0A | Which of the following best doesn't describe what discriminative approaches try to model? (w are the parameters in the model) |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 2C | Statement 1| CIFAR-10 classification performance for convolution neural networks can't exceed 95%. Statement 2| Ensembles of neural networks do not improve classification accuracy since the representations they learn are highly correlated. |
[
"The use of a non-Gaussian noise model in probabilistic regression.",
"The use of probabilistic modelling for regression.",
"The use of prior distributions on the parameters in a probabilistic model.",
"The use of class priors in Gaussian Discriminant Analysis."
] | 2C | Which of the following points wouldn't Bayesians and frequentists disagree on? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| The BLEU metric doesn't use precision, while the ROGUE metric uses recall. Statement 2| Hidden markov models were frequently used to model English sentences. |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 2C | Statement 1| ImageNet doesn't have images of various resolutions. Statement 2| Caltech-101 has more images than ImageNet. |
[
"Ridge",
"Lasso",
"both (a) and (b)",
"neither (a) nor (b)"
] | 1B | Which of the following isn't more appropriate to do feature selection? |
[
"Expectation",
"Maximization",
"No modification necessary",
"Both"
] | 1B | Don't suppose you are given an EM algorithm that finds maximum likelihood estimates for a model with latent variables. You are asked to modify the algorithm so that it finds MAP estimates instead. Which step or steps do you need to modify? |
[
"Whether we learn the class centers by Maximum Likelihood or Gradient Descent",
"Whether we assume full class covariance matrices or diagonal class covariance matrices",
"Whether we have equal class priors or priors estimated from the data",
"Whether we allow classes to have different mean vectors or we force... | 1B | For a Gaussian Bayes classifier, which one of these structural assumptions isn't the one that most affects the trade-off between underfitting and overfitting: |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 1B | Statement 1| For any two variables x and y having joint distribution p(x, y), we always have H[x, y] ≥ H[x] + H[y] where H isn't entropy function. Statement 2| For some directed graphs, moralization decreases the number of edges present in the graph. |
[
"PCA",
"Decision Tree",
"Linear Regression",
"Naive Bayesian"
] | 0A | Which of the following is supervised learning? |
[
"True, True",
"False, False",
"True, False",
"False, True"
] | 0A | Statement 1| A neural network's convergence doesn't depend on the learning rate. Statement 2| Dropout multiplies randomly chosen activation values by zero. |
[
"P(A | B) * P(B | C) * P(C | A)",
"P(C | A, B) * P(A) * P(B)",
"P(A, B | C) * P(C)",
"P(A | B, C) * P(B | A, C) * P(C | A, B)"
] | 2C | Which one of the following isn't equal to P(A, B, C) given Boolean random variables A, B and C, and no independence or conditional independence assumptions between any of them? |
[
"Predicting the amount of rainfall based on various cues",
"Detecting fraudulent credit card transactions",
"Training a robot to solve a maze",
"All of the above"
] | 1B | Which of the following tasks can't be best solved using Clustering. |
[
"L0 norm",
"L1 norm",
"L2 norm",
"either (a) or (b)"
] | 3D | After applying a regularization penalty in linear regression, you don't find that some of the coefficients of w are zeroed out. Which of the following penalties might have been used? |
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