A) The data is labeled, meaning each example is paired with a target output. B) The data is generated randomly by the algorithm. C) The data is unlabeled, and the model must find patterns on its own. D) The data is generated randomly by the algorithm.
A) Reduce the dimensionality of the input data for visualization. B) Memorize the entire training dataset perfectly. C) Generalize from the training data to make accurate predictions on new, unseen data. D) Discover hidden patterns without any guidance.
A) The loss function. B) The label or target output. C) The input features. D) The model's parameters.
A) Forecasting the temperature for tomorrow. B) Diagnosing a tumor as malignant or benign based on medical images. C) Estimating the annual revenue of a company. D) Predicting the selling price of a house based on its features.
A) Clustering problem. B) Dimensionality reduction problem. C) Regression problem. D) Classification problem.
A) To classify emails into spam and non-spam folders. B) To achieve perfect accuracy on a held-out test set. C) To predict a target variable based on labeled examples. D) To discover the inherent structure, patterns, or relationships within unlabeled data.
A) Classification. B) Regression. C) Reinforcement Learning. D) Clustering.
A) A support vector machine for classification. B) Clustering, a type of unsupervised learning. C) Logistic Regression, a type of supervised learning. D) Linear Regression, a type of supervised learning.
A) Assign categorical labels to each data point. B) Predict a continuous output variable. C) Increase the number of features to improve model accuracy. D) Reduce the number of features while preserving the most important information in the data.
A) Classification in supervised learning. B) Regression in supervised learning. C) Deep learning with neural networks. D) Association rule learning in unsupervised learning.
A) It is always more accurate than fully supervised learning. B) Labeling data is often expensive and time-consuming, so it leverages a small labeled set with a large unlabeled set. C) It requires no labeled data at all. D) It is simpler to implement than unsupervised learning.
A) "How much?" or "How many?" B) "Is this pattern anomalous?" C) "Which category?" D) "What is the underlying group?"
A) "Which category?" or "What class?" B) "What is the correlation between these variables?" C) "How can I reduce the number of features?" D) "How much?" or "How many?"
A) Logistic Regression B) Decision Tree for classification C) Linear Regression D) k-Nearest Neighbors for classification
A) Multi-class classification B) Dimensionality reduction C) Clustering D) Regression
A) The average value of a continuous target B) The final class labels or decisions C) The probability of moving to the next node D) The input features for a new data point
A) A random number B) A categorical class label C) The name of the feature used for splitting D) A continuous value, often the mean of the target values of the training instances that reach the leaf
A) Interpretability; the model's decision-making process is easy to understand and visualize B) Superior performance on all types of data compared to other algorithms C) Immunity to overfitting on noisy datasets D) Guarantee to find the global optimum for any dataset
A) Grow a tree structure by making sequential decisions B) Initialize the weights of a neural network C) Find a linear separating hyperplane in a high-dimensional feature space, even when the data is not linearly separable in the original space D) Perform linear regression more efficiently
A) The weights of a neural network layer B) All data points in the training set C) The axes of the original feature space D) . Data points that are closest to the decision boundary and most critical for defining the optimal hyperplane
A) Their superior interpretability and simplicity B) Their effectiveness in high-dimensional spaces and their ability to model complex, non-linear decision boundaries C) Their inherent resistance to any form of overfitting D) Their lower computational cost for very large datasets
A) Clustering B) Training or model fitting C) Dimensionality reduction D) Data preprocessing
A) There are no ground truth labels to compare the results against B) The data is always too small C) The algorithms are not well-defined D) The models are always less accurate than supervised models
A) Dimensionality Reduction techniques like Principal Component Analysis (PCA) B) A Regression algorithm like Linear Regression C) An Association rule learning algorithm D) A Classification algorithm like Logistic Regression
A) Classification, a supervised learning method B) Clustering, an unsupervised learning method C) Regression, a supervised learning method D) A neural network for image recognition
A) Principal component B) Support vector C) Artificial neuron or perceptron, which receives inputs, applies a transformation, and produces an output D) Decision node in a tree
A) Activation function B) Optimization algorithm C) Kernel function D) Loss function
A) A constant function B) The mean squared error function C) Rectified Linear Unit (ReLU) D) The identity function (f(x) = x)
A) Manually setting the weights based on expert knowledge B) Iteratively adjusting the weights and biases to minimize a loss function C) Randomly assigning weights and never changing them D) Clustering the input data
A) Efficiently calculate the gradient of the loss function with respect to all the weights in the network, enabling the use of gradient descent B) Visualize the network's architecture C) Perform clustering on the output layer D) Initialize the weights before training
A) K-means clustering exclusively B) Decision trees with a single split C) Simple linear regression models D) Neural networks with many layers (hence "deep")
A) Always train faster and with less data B) Be perfectly interpretable, like a decision tree C) Operate without any need for data preprocessing D) Automatically learn hierarchical feature representations from data
A) Unsupervised clustering of audio signals B) Tabular data with many categorical features C) Image data, due to their architecture which exploits spatial locality D) Text data and natural language processing
A) Flatten the input into a single vector B) Perform the final classification C) Initialize the weights of the network D) Detect local features (like edges or textures) in the input by applying a set of learnable filters
A) Static, non-temporal data B) Independent and identically distributed (IID) data points C) Sequential data, like time series or text, due to their internal "memory" of previous inputs D) Only image data
A) The gradients becoming too large and causing numerical instability B) The model overfitting to the training data C) The loss function reaching a perfect value of zero D) The gradients becoming exceedingly small as they are backpropagated through many layers, which can halt learning in early layers
A) Fit the model's parameters (e.g., the weights in a neural network) B) Deploy the model in a production environment C) Tune the model's hyperparameters D) Provide an unbiased evaluation of a final model's performance
A) Tuning hyperparameters and making decisions about the model architecture during development B) Data preprocessing and cleaning C) The initial training of the model's weights D) The final, unbiased assessment of the model's generalization error
A) Used repeatedly to tune the model's hyperparameters B) Used only once, for a final evaluation of the model's performance on unseen data after model development is complete C) Used repeatedly to tune the model's hyperparameters D) Ignored in the machine learning pipeline
A) Fails to learn the underlying pattern in the training data B) Is too simple to capture the trends in the data C) Learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data D) Is evaluated using the training set instead of a test set
A) Increasing the model's capacity by adding more layers B) Training for more epochs without any checks C) Dropout, which randomly ignores a subset of neurons during training D) Using a smaller training dataset
A) The weights connecting the input layer to the hidden layer B) The error from sensitivity to small fluctuations in the training set, leading to overfitting C) The error from erroneous assumptions in the learning algorithm, leading to underfitting D) The activation function used in the output layer
A) The error from erroneous assumptions in the learning algorithm, leading to underfitting B) The error from sensitivity to small fluctuations in the training set, leading to overfitting C) The speed at which the model trains D) The intercept term in a linear regression model
A) Decreasing bias will typically increase variance, and vice versa. The goal is to find a balance B) Bias and variance can be minimized to zero simultaneously C) Only variance is important for model performance D) Only bias is important for model performance
A) A well-generalized model B) Perfect model performance C) Overfitting D) Underfitting
A) The speed of the backpropagation algorithm B) How well the model is performing on the training data; it's the quantity we want to minimize during training C) The number of layers in the network D) The accuracy on the test set
A) Iteratively adjusts parameters in the direction that reduces the loss function B) Is only used for unsupervised learning C) Guarantees finding the global minimum for any loss function D) Randomly searches the parameter space for a good solution
A) The size of the step taken during each parameter update. A rate that is too high can cause divergence, while one that is too low can make training slow B) The number of layers in a neural network C) The activation function for the output layer D) The amount of training data used in each epoch
A) One complete pass of the entire training dataset through the learning algorithm B) The final evaluation on the test set C) A type of regularization technique D) The processing of a single training example
A) The number of layers in the network B) The total number of examples in the training set C) The number of training examples used in one forward/backward pass before the model's parameters are updated D) The number of validation examples |