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