What term refers to the ability to understand and interpret machine learning model decisions?
- Explainability
- Predictability
- Efficiency
- Generalization
Explainability is the term that refers to the ability to understand and interpret machine learning model decisions. It's crucial for trust and accountability in AI systems.
Which variant of RNN is designed to better capture long-term dependencies in sequence data?
- Vanilla RNN
- Bidirectional RNN
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
Long Short-Term Memory (LSTM) is designed to capture long-term dependencies in sequences by maintaining cell states that are less susceptible to the vanishing gradient problem.
An online platform wants to generate new, realistic profile pictures for users who don't want to upload their own photos. They aim for these generated images to be indistinguishable from real photos. Which technology can achieve this?
- Generative Adversarial Networks (GAN)
- Principal Component Analysis (PCA)
- Logistic Regression
- K-Means Clustering
Generative Adversarial Networks (GAN) are designed to generate synthetic data that is highly realistic. In the context of generating profile pictures, GANs can produce images that are often indistinguishable from real photos, making them an ideal choice for this task.
The main advantage of Deep Q Networks over traditional Q-learning is their ability to handle high-dimensional ________ spaces.
- State
- Action
- Observation
- Feature
Deep Q Networks are advantageous due to their capability to handle high-dimensional observation spaces. This is crucial when dealing with complex real-world data, as in image-based environments.
A model that makes decisions without being able to provide clear reasoning behind them lacks ________.
- Transparency
- Performance
- Speed
- Scalability
Transparency in a model is the ability to explain its decision-making, which is crucial for trust, auditing, and regulatory compliance.
A hospital is trying to reduce the readmission rates of patients. They decide to use historical patient data, including treatment details, doctor's notes, and patient feedback. Which machine learning approach in healthcare would be most suitable for this?
- Natural Language Processing (NLP)
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
Natural Language Processing (NLP) is the most suitable approach for extracting insights from textual data like doctor's notes and patient feedback, which can help in reducing readmission rates.
What is the primary challenge addressed by the multi-armed bandit problem?
- Balancing Exploration and Exploitation
- Image Recognition
- Language Translation
- Voice Assistant Development
The primary challenge of the multi-armed bandit problem is to balance Exploration (trying new actions) and Exploitation (choosing known good actions) to maximize cumulative rewards in a limited time.
The ________ classifier calculates the probability of a particular event based on prior knowledge of conditions related to that event.
- Decision Tree
- K-Means
- Naive Bayes
- Support Vector Machine
The Naive Bayes classifier calculates the probability of an event based on prior knowledge of conditions. It's a probabilistic classifier.
When determining the number of clusters (K) for K-means clustering, which method involves plotting the variance as K increases and looking for an "elbow" in the plot?
- Davies-Bouldin Index
- Elbow Method
- Gap Statistics
- Silhouette Analysis
The Elbow Method involves plotting the variance (inertia) against different values of K. The "elbow" point is where the rate of decrease sharply changes, indicating an optimal K.
The balance between taking known rewarding actions and discovering new ones is termed as the ________ dilemma.
- Exploration-Exploitation
- Action-Selection
- Reinforcement-Reward
- Decision-Making
This is known as the Exploration-Exploitation dilemma, which is central in Reinforcement Learning, referring to the balance between exploiting known actions for rewards and exploring new actions to discover more rewarding ones.