A) Offers a balance between the efficiency of batch GD and the robustness of SGD. B) Is only applicable to linear models. C) Does not require a loss function. D) Is guaranteed to converge faster than any other method.
A) Training a model from scratch on every new problem. B) Taking a model pre-trained on a large dataset (e.g., ImageNet) and fine-tuning it for a new, specific task with a smaller dataset. C) Using only unsupervised learning techniques. D) Forgetting everything a model has learned.
A) Reinforcement learning. B) Predicting continuous values in a regression task. C) Unsupervised learning tasks like dimensionality reduction and data denoising.b D) Supervised classification of images.
A) A convolutional layer followed by an RNN layer. B) An encoder that compresses the input and a decoder that reconstructs the input from the compression. C) Only a single layer of perceptrons. D) A single output neuron with a linear activation.
A) The proportion of actual positives that were identified correctly. B) The harmonic mean of precision and recall. C) The proportion of total predictions that were correct. D) The proportion of positive identifications that were actually correct.
A) The cost of false positives is high (e.g., in spam detection, where you don't want to flag legitimate emails as spam). B) The cost of false negatives is high (e.g., in disease screening, where you don't want to miss a sick patient). C) You need a single metric that combines precision and recall. D) You are evaluating a regression model.
A) You are evaluating a clustering model. B) You need a single metric that combines precision and recall. C) The cost of false negatives is high (e.g., in disease screening, where you don't want to miss a sick patient). D) The cost of false positives is high (e.g., in spam detection).
A) The difference between precision and recall. B) The arithmetic mean of precision and recall. C) A metric used exclusively for regression. D) The harmonic mean of precision and recall, providing a single score that balances both concerns.
A) The total number of misclassified instances. B) The average of the squares of the errors between predicted and actual values. C) The variance of the input features. D) The accuracy of a classification model.
A) The clustering quality of a K-means algorithm. B) The loss of a regression model over time. C) The performance of a binary classification model at various classification thresholds. D) The architecture of a neural network.
A) 0.0. B) -1.0. C) 1.0. D) 0.5.
A) Increase the size of the training dataset. B) Replace the need for a separate test set. C) Obtain a more robust estimate of model performance by training and evaluating the model K times on different splits of the data. D) Visualize high-dimensional data.
A) A random selection from the training set. B) A single, pre-defined rule. C) The majority vote among its K closest neighbors in the feature space. D) The output of a linear function.u
A) Is always set to 1 for the best performance. B) Controls the model's flexibility. A small K can lead to overfitting, while a large K can lead to underfitting. C) Is the number of features in the dataset. D) Is the learning rate for the algorithm.
A) Classifying data using a decision boundary. B) Finding new, uncorrelated dimensions (principal components) that capture the maximum variance in the data.u C) Predicting a target variable using linear combinations of features. D) Clustering data into K groups.
A) Captures the greatest possible variance in the data. B) Is perpendicular to all other components. C) Captures the least possible variance in the data. D) Is randomly oriented.
A) The data is projected onto a single dimension. B) The between-cluster variance is minimized. C) The within-cluster variance is minimized. D) The data is perfectly classified into known labels.
A) Help choose the optimal number of clusters K by looking for a "bend" in the plot of within-cluster variance. B) Initialize the cluster centroids. C) Determine the learning rate for gradient descent. D) Evaluate the accuracy of a classification model.
A) Always have the lowest possible accuracy. B) Do not use probability in their predictions. C) Make a strong (naive) assumption that all features are conditionally independent given the class label. D) Are very simple and cannot handle complex data.
A) Regression algorithm for predicting continuous values. B) Classification algorithm that models the probability of a binary outcome using a logistic function. C) Dimensionality reduction technique. D) Clustering algorithm for grouping unlabeled data.
A) Number of features in the input. B) Distance to the decision boundary. C) Probability that the input belongs to a particular class. D) Exact value of the target variable.
A) Support Vector Machines. B) Linear Regression models. C) Decision Trees to reduce overfitting and improve generalization. D) K-NN models.
A) Perform feature extraction like PCA. B) Reduce variance by training individual trees on random subsets of the data and averaging their results C) Increase the speed of a single decision tree. D) Reduce bias by making trees more complex.
A) Build all models independently and average them. B) Do not require any parameter tuning. C) Are exclusively used for unsupervised learning. D) Build models sequentially, where each new model corrects the errors of the previous ones.
A) The evaluation of a model's final performance. B) The process of using domain knowledge to create new input features that make machine learning algorithms work better. C) The process of deleting all features from a dataset. D) The automatic learning of features by a deep neural network.
A) Cluster similar data points together. B) Normalize continuous numerical features. C) Reduce the dimensionality of image data. D) Convert categorical variables into a binary (0/1) format that can be provided to ML algorithms.
A) Are based on tree-based models like Decision Trees and Random Forests. B) Are based on distance calculations or gradient descent, such as SVM and Neural Networks. C) Are used for clustering only. D) Are used for association rule learning.
A) All datasets should have as many features as possible. B) Dimensionality reduction always improves model performance. C) There are never enough features to train a good model. D) As the number of features grows, the data becomes increasingly sparse, making it harder to find meaningful patterns.
A) Make models more complex to fit the training data better. B) Speed up the training time of a model. C) Increase the variance of a model. D) Prevent overfitting by adding a penalty term to the loss function that discourages complex models.
A) Increased model complexity. B) Sparse models where the weights of less important features are driven to zero, effectively performing feature selection. C) A decrease in model interpretability. D) All features having non-zero weights.
A) The output predictions of the model. B) Configuration settings for the learning algorithm that are not learned from the data and must be set prior to training (e.g., learning rate, K in K-NN). C) The input features of the model. D) The parameters that the model learns during training (e.g., weights in a neural network).
A) Searching for the best combination of hyperparameters that results in the best model performance. B) Cleaning the raw data. C) Training the model's internal weights. D) Deploying the final model.
A) Exhaustively searching over a specified set of hyperparameter values. B) Ignoring hyperparameters altogether. C) Using a separate neural network to predict the best hyperparameters. D) Randomly sampling hyperparameter combinations from a distribution.
A) Starting the training process later than scheduled. B) Using a very small learning rate. C) Halting the training process when performance on a validation set starts to degrade, indicating the onset of overfitting. D) Stopping the training after a fixed, very short number of epochs.
A) Convolutional layers for processing images. B) Recurrent layers for processing sequences. C) Fully connected layers, where each neuron in one layer is connected to every neuron in the next layer. D) A single layer of neurons.
A) Binary classification problems. B) Regression problems. C) Multi-class classification problems, as it outputs a probability distribution over the possible classes. D) Unsupervised learning problems.
A) Is only used for unsupervised learning. B) Does not require any hyperparameters. C) Is guaranteed to find the global minimum for any function. D) Combines the advantages of two other extensions of stochastic gradient descent, AdaGrad and RMSProp.
A) Replace the need for an activation function. B) Increase the batch size during training. C) Improve the stability and speed of neural network training by normalizing the inputs to each layer. D) Normalize the entire dataset before feeding it into the network.
A) Clustering algorithm's group assignments. B) Classification model on a set of test data for which the true values are known. C) Dimensionality reduction technique's effectiveness. D) Regression model's accuracy.
A) The model correctly predicted the positive class. B) The model incorrectly predicted the positive class. C) The model correctly predicted the negative class. D) The model incorrectly predicted the negative class.
A) The learning rate is set too high. B) One class in the training data has significantly more examples than another, which can bias the model. C) The model is too complex for the data. D) The features are not scaled properly.
A) Ignores the minority class completely. B) Deletes examples from the majority class at random. C) Combines all classes into one. D) Generates synthetic examples for the minority class to balance the dataset.
A) It is only used for clustering unlabeled data. B) It is a simpler and less powerful approach. C) It learns by interacting with an environment and receiving rewards or penalties for actions, without a labeled dataset. D) It requires a fully labeled dataset for training.
A) A decision tree for classification. B) A clustering of possible actions. C) A policy that tells an agent what action to take under what circumstances by learning a value function. D) The principal components of a state space.
A) Generating new, original text without any input. B) Reducing the dimensionality of word vectors. C) Grouping similar news articles without labels. D) Sentiment analysis, where text is classified as positive, negative, or neutral.
A) Are a type of clustering algorithm. B) Represent words as simple one-hot encoded vectors. C) Are used only for image classification. D) Represent words as dense vectors in a continuous space, capturing semantic meaning.
A) A Generator and a Discriminator, which are trained in opposition to each other. B) Two identical Convolutional Neural Networks. C) An Encoder and a Decoder for compression. D) A single, large Regression network.
A) Creating new, synthetic data that is indistinguishable from real data. B) Reducing the dimensionality of the input. C) Classifying input images into categories. D) Discriminating between real and fake data.
A) Binary classifier that tries to correctly label data as real (from the dataset) or fake (from the generator). B) Regression model predicting a continuous value. C) Dimensionality reduction technique. D) Clustering algorithm grouping similar images. |