In a scenario with noisy data, increasing the value of 'k' in the k-NN algorithm can help to ________ the noise.
- Amplify
- Eliminate
- Introduce
- Preserve
Increasing the value of 'k' in the k-NN algorithm can help eliminate noise. A higher 'k' value smooths the decision boundaries, reducing the impact of individual noisy data points.
Q-learning is a type of ________ learning algorithm that aims to find the best action to take given a current state.
- Reinforcement
- Supervised
- Unsupervised
- Semi-supervised
Q-learning is a type of reinforcement learning that focuses on finding the best action to take in a given state to maximize cumulative rewards.
When aiming to reduce both bias and variance, one might use techniques like ________ to regularize a model.
- Cross-Validation
- Data Augmentation
- Dropout
- L1 Regularization
L1 regularization is a technique used to reduce both bias and variance in a machine learning model. It does so by adding a penalty term to the model's loss function, which encourages the model to use fewer features, thus reducing complexity and variance. Dropout, Cross-Validation, and Data Augmentation are techniques but are not primarily used for regularization.
What does the "G" in GRU stand for when referring to a type of RNN?
- Gated
- Global
- Gradient
- Graph
The "G" in GRU stands for "Gated." GRU is a type of RNN that uses gating mechanisms to control information flow, making it capable of handling sequences efficiently.
One of the challenges in training deep RNNs is the ________ gradient problem, which affects the network's ability to learn long-range dependencies.
- Vanishing
- Exploding
- Overfitting
- Regularization
The vanishing gradient problem refers to the issue where gradients in deep RNNs become too small during training, making it challenging to capture long-range dependencies.
In the context of the bias-variance trade-off, which one is typically associated with complex models with many parameters?
- Balanced Bias-Variance
- High Bias
- High Variance
- Neither
High Variance is typically associated with complex models with many parameters. Complex models are more flexible and tend to fit the training data closely, resulting in high variance, which can lead to overfitting.
Imagine a scenario where an online learning platform wants to categorize its vast number of courses into different topics. The platform doesn't have predefined categories but wants the algorithm to determine them based on course content. This task would best be accomplished using which learning approach?
- Clustering
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
Unsupervised learning is the most suitable approach. Here, the algorithm should discover inherent structures or clusters within the courses without any predefined categories, making unsupervised learning a fitting choice.
When visualizing high-dimensional data in two or three dimensions, one might use PCA to project the data onto the first few ________.
- Principal Components
- Data Points
- Dimensions
- Eigenvalues
PCA (Principal Component Analysis) is used to reduce the dimensionality of data by projecting it onto the first few Principal Components, which are linear combinations of the original dimensions.
For a medical test, it's crucial to minimize the number of false negatives. Which metric would be particularly important to optimize in this context?
- Accuracy
- F1 Score
- Precision
- Recall
In the context of a medical test, minimizing false negatives is vital because you want to avoid missing actual positive cases. This emphasizes the importance of optimizing recall, which measures the ability of the test to correctly identify all positive cases, even at the expense of more false positives.
In hierarchical clustering, the ________ method involves merging the closest clusters in each iteration.
- Agglomerative
- Divisive
- DBSCAN
- OPTICS
In hierarchical clustering, the Agglomerative method starts with individual data points as clusters and iteratively merges the closest clusters, creating a hierarchy. "Agglomerative" is the correct option, representing the bottom-up approach of this clustering method.