A) The data is labeled, meaning each example is paired with a target output. B) The data is unlabeled, and the model must find patterns on its own C) The data is generated randomly by the algorithm. D) The data is always image-based.
A) Discover hidden patterns without any guidance B) Memorize the entire training dataset perfectly. C) Reduce the dimensionality of the input data for visualization. D) Generalize from the training data to make accurate predictions on new, unseen data.
A) The label or target output. B) The input features. C) The loss function D) The model's parameters.
A) Diagnosing a tumor as malignant or benign based on medical images. B) Estimating the annual revenue of a company. C) Predicting the selling price of a house based on its features. D) Forecasting the temperature for tomorrow.
A) Dimensionality reduction problem B) Clustering problem. C) Classification problem D) Regression 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) Reinforcement Learning. B) Classification C) Regression D) Clustering
A) Clustering, a type of unsupervised learning. B) Logistic Regression, a type of supervised learning. C) A support vector machine for classification. D) Linear Regression, a type of supervised learning.
A) Predict a continuous output variable. B) Assign categorical labels to each data point. C) Reduce the number of features while preserving the most important information in the data. D) Increase the number of features to improve model accuracy.
A) Association rule learning in unsupervised learning. B) Deep learning with neural networks. C) Classification in supervised learning. D) Regression in supervised learning.
A) It requires no labeled data at all. B) It is always more accurate than fully supervised learning. C) It is simpler to implement than unsupervised learning. D) Labeling data is often expensive and time-consuming, so it leverages a small labeled set with a large unlabeled set.
A) "Which category?" B) "How much?" or "How many?" C) "Is this pattern anomalous?" D) "What is the underlying group?"
A) "What is the correlation between these variables?" B) "How much?" or "How many?" C) "How can I reduce the number of features?" D) "Which category?" or "What class?"
A) Logistic Regression. B) Linear Regression. C) Decision Tree for classification. D) k-Nearest Neighbors for classification.
A) Multi-class classification. B) Clustering. C) Dimensionality reduction. D) Regression.
A) The probability of moving to the next node. B) The average value of a continuous target. C) The input features for a new data point. D) The final class labels or decisions.
A) The name of the feature used for splitting. B) A continuous value, often the mean of the target values of the training instances that reach the leaf. C) A random number. D) A categorical class label.
A) Guarantee to find the global optimum for any dataset. B) Superior performance on all types of data compared to other algorithms. C) Immunity to overfitting on noisy datasets. D) Interpretability; the model's decision-making process is easy to understand and visualize.
A) Perform linear regression more efficiently. B) Initialize the weights of a neural network. C) Grow a tree structure by making sequential decisions. D) Find a linear separating hyperplane in a high-dimensional feature space, even when the data is not linearly separable in the original space.
A) All data points in the training set. B) Data points that are closest to the decision boundary and most critical for defining the optimal hyperplane. C) The axes of the original feature space. D) The weights of a neural network layer.
A) Their inherent resistance to any form of overfitting. B) Their superior interpretability and simplicity. C) Their effectiveness in high-dimensional spaces and their ability to model complex, non-linear decision boundaries. D) Their lower computational cost for very large datasets.
A) Clustering. B) Dimensionality reduction. C) Training or model fitting. D) Data preprocessing.
A) The algorithms are not well-defined. B) There are no ground truth labels to compare the results against. C) The data is always too small. D) The models are always less accurate than supervised models.
A) An Association rule learning algorithm. B) Dimensionality Reduction techniques like Principal Component Analysis (PCA). C) A Regression algorithm like Linear Regression. D) A Classification algorithm like Logistic Regression.
A) A neural network for image recognition. B) Regression, a supervised learning method. C) Clustering, an unsupervised learning method. D) Classification, a supervised learning method.
A) Support vector. B) Decision node in a tree. C) Artificial neuron or perceptron, which receives inputs, applies a transformation, and produces an output. D) Principal component.
A) Kernel function. B) Activation function. C) Loss function. D) Optimization algorithm.
A) The mean squared error function. B) A constant function. C) The identity function (f(x) = x). D) Rectified Linear Unit (ReLU).
A) Clustering the input data. B) Manually setting the weights based on expert knowledge. C) Randomly assigning weights and never changing them. D) Iteratively adjusting the weights and biases to minimize a loss function.
A) Visualize the network's architecture. B) Efficiently calculate the gradient of the loss function with respect to all the weights in the network, enabling the use of gradient descent. C) Perform clustering on the output layer. D) Initialize the weights before training.
A) Simple linear regression models. B) Decision trees with a single split. C) K-means clustering exclusively. D) Neural networks with many layers (hence "deep").
A) Be perfectly interpretable, like a decision tree. B) Operate without any need for data preprocessing. C) Automatically learn hierarchical feature representations from data. D) Always train faster and with less data.
A) Text data and natural language processing. B) Unsupervised clustering of audio signals. C) Image data, due to their architecture which exploits spatial locality. D) Tabular data with many categorical features.
A) Perform the final classification. B) Flatten the input into a single vector. C) Detect local features (like edges or textures) in the input by applying a set of learnable filters. D) Initialize the weights of the network.
A) Static, non-temporal data. B) Sequential data, like time series or text, due to their internal "memory" of previous inputs. C) Independent and identically distributed (IID) data points. D) Only image data.
A) The gradients becoming exceedingly small as they are backpropagated through many layers, which can halt learning in early layers. B) The gradients becoming too large and causing numerical instability. C) The model overfitting to the training data. D) The loss function reaching a perfect value of zero.
A) Tune the model's hyperparameters. B) Deploy the model in a production environment. C) Fit the model's parameters (e.g., the weights in a neural network). D) Provide an unbiased evaluation of a final model's performance.
A) Tuning hyperparameters and making decisions about the model architecture during development. B) The final, unbiased assessment of the model's generalization error. C) The initial training of the model's weights. D) Data preprocessing and cleaning.
A) Used repeatedly to tune the model's hyperparameters. B) Ignored in the machine learning pipeline. C) Used only once, for a final evaluation of the model's performance on unseen data after model development is complete. D) Used as part of the training data to improve accuracy.
A) Is evaluated using the training set instead of a test set. B) Learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data. C) Fails to learn the underlying pattern in the training data. D) Is too simple to capture the trends in the data.
A) Dropout, which randomly ignores a subset of neurons during training. B) Training for more epochs without any checks. C) Increasing the model's capacity by adding more layers. D) Using a smaller training dataset.
A) The error from sensitivity to small fluctuations in the training set, leading to overfitting. B) The weights connecting the input layer to the hidden layer. C) The error from erroneous assumptions in the learning algorithm, leading to underfitting. D) The activation function used in the output layer.
A) The intercept term in a linear regression model. B) The speed at which the model trains. C) The error from sensitivity to small fluctuations in the training set, leading to overfitting. D) The error from erroneous assumptions in the learning algorithm, leading to underfitting.
A) Bias and variance can be minimized to zero simultaneously. B) Only variance is important for model performance. C) Decreasing bias will typically increase variance, and vice versa. The goal is to find a balance. D) Only bias is important for model performance.
A) Perfect model performance. B) Overfitting. C) Underfitting. D) A well-generalized model.
A) The speed of the backpropagation algorithm. B) The number of layers in the network. C) How well the model is performing on the training data; it's the quantity we want to minimize during training. D) The accuracy on the test set.
A) Iteratively adjusts parameters in the direction that reduces the loss function. B) Guarantees finding the global minimum for any loss function. C) Randomly searches the parameter space for a good solution. D) Is only used for unsupervised learning.
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 activation function for the output layer. C) The amount of training data used in each epoch. D) The number of layers in a neural network.
A) The final evaluation on the test set. B) The processing of a single training example. C) A type of regularization technique. D) One complete pass of the entire training dataset through the learning algorithm.
A) The number of validation examples. B) The number of training examples used in one forward/backward pass before the model's parameters are updated. C) The total number of examples in the training set. D) The number of layers in the network.
A) The entire training set. B) Exactly 50% of the training set. C) 1, meaning the parameters are updated after each individual training example. D) A random number between 1 and 100. |