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.
In the context of GANs, the generator tries to produce fake data, while the discriminator tries to ________ between real and fake data.
- Differentiate
- Discriminate
- Generate
- Classify
The discriminator in a GAN is responsible for distinguishing or discriminating between real and fake data, not generating or differentiating.
What type of neural network is designed for encoding input data into a compressed representation and then decoding it back to its original form?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Autoencoder
- Long Short-Term Memory (LSTM)
Autoencoders are neural networks designed for this task. They consist of an encoder network that compresses input data into a compact representation and a decoder network that reconstructs the original data from this representation.
A finance company wants to analyze sequences of stock prices to predict future market movements. Given the long sequences of data, which RNN variant would be more suited to capture potential long-term dependencies in the data?
- Simple RNN
- Bidirectional RNN
- Gated Recurrent Unit (GRU)
- Long Short-Term Memory (LSTM)
A Long Short-Term Memory (LSTM) is a suitable choice for capturing long-term dependencies in stock price sequences. LSTM's memory cell and gating mechanisms make it capable of handling long sequences and understanding potential trends in financial data.
How does ICA differ from Principal Component Analysis (PCA) in terms of data independence?
- ICA finds statistically independent components
- PCA finds orthogonal components
- ICA finds the most significant features
- PCA reduces dimensionality
Independent Component Analysis (ICA) seeks statistically independent components, meaning they are as unrelated as possible, while PCA seeks orthogonal components that explain the most variance but are not necessarily independent. ICA focuses on data independence, making it suitable for source separation tasks.
In reinforcement learning, what term describes the dilemma of choosing between trying out new actions and sticking with known actions that work?
- Exploration-Exploitation Dilemma
- Action Selection Dilemma
- Reinforcement Dilemma
- Policy Dilemma
The Exploration-Exploitation Dilemma is the challenge of balancing exploration (trying new actions) with exploitation (using known actions). It's crucial in RL for optimal decision-making.
How do the generator and discriminator components of a GAN interact during training?
- The generator produces real data.
- The discriminator generates fake data.
- The generator tries to fool the discriminator.
- The discriminator generates real data.
In a GAN (Generative Adversarial Network), the generator creates fake data to deceive the discriminator, which aims to distinguish between real and fake data. This adversarial process improves the quality of the generated data.
A company wants to develop a chatbot that learns how to respond to customer queries by interacting with them and getting feedback. The chatbot should improve its responses over time based on this feedback. This is an application of which type of learning?
- Online Learning
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
This is an application of reinforcement learning. In reinforcement learning, an agent interacts with its environment and learns to make decisions to maximize a reward signal. The chatbot improves based on feedback (rewards) received.
When regular Q-learning takes too much time to converge in a high-dimensional state space (e.g., autonomous vehicle parking), what modification could help it learn faster?
- Deep Q-Networks (DQNs)
- Policy Gradient Methods
- Fitted Q-Iteration (FQI)
- Temporal Difference (TD) Learning
Using Deep Q-Networks (DQNs) is a modification of Q-learning, which employs neural networks to handle high-dimensional state spaces efficiently. DQNs can approximate the Q-values, expediting learning in complex environments.