A robot is navigating a maze. Initially, it often runs into walls or dead-ends, but over time it starts finding the exit more frequently. To achieve this, the robot likely emphasized ________ in the beginning and shifted towards ________ over time.
- Exploration, Exploitation
- Breadth-First Search
- Depth-First Search
- A* Search
In the context of reinforcement learning, the robot employs "exploration" initially to discover the maze, and as it learns, it shifts towards "exploitation" to choose actions that yield higher rewards, like finding the exit.
In reinforcement learning, the agent learns a policy which maps states to ________.
- Actions
- Rewards
- Values
- Policies
In reinforcement learning, the agent learns a policy that maps states to optimal actions, hence filling in the blank with "Policies" is accurate. This policy helps the agent make decisions in various states.
You are working on a dataset with a large number of features. While some of them seem relevant, many appear to be redundant or irrelevant. What technique would you employ to enhance model performance and interpretability?
- Data Normalization
- Feature Scaling
- Principal Component Analysis (PCA)
- Recursive Feature Elimination (RFE)
Principal Component Analysis (PCA) is a dimensionality reduction technique that can help reduce the number of features while preserving the most important information. It enhances model performance by eliminating redundant features and improves interpretability by transforming the data into a new set of uncorrelated variables.
Which term describes a model that has been trained too closely to the training data and may not perform well on new, unseen data?
- Bias
- Generalization
- Overfitting
- Underfitting
Overfitting is a common issue in machine learning where a model becomes too specialized to the training data and fails to generalize well to new data. It's essential to strike a balance between fitting the training data and generalizing to unseen data.
Which RNN architecture is more computationally efficient but might not capture all the intricate patterns that its counterpart can: LSTM or GRU?
- GRU
- LSTM
- Both capture patterns efficiently
- Neither captures patterns effectively
The GRU (Gated Recurrent Unit) is more computationally efficient than LSTM (Long Short-Term Memory) but may not capture all intricate patterns in data due to its simplified architecture. LSTM is more expressive but computationally demanding.
In the context of machine learning, what is the primary concern of fairness?
- Bias
- Overfitting
- Underfitting
- Feature Selection
The primary concern in fairness within machine learning is 'Bias.' Bias can lead to unequal treatment or discrimination, especially when making predictions in sensitive areas like lending or hiring.
In ________ learning, algorithms are trained on labeled data, where the answer key is provided.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
In supervised learning, algorithms are trained using labeled data, which means each input is associated with the correct answer. This helps the algorithm learn and make predictions or classifications.
Experience replay, often used in DQNs, helps in stabilizing the learning by doing what?
- Reducing Correlation between Data
- Speeding up convergence
- Improving Exploration
- Saving Memory Space
Experience replay in DQNs reduces the correlation between consecutive data samples, which stabilizes learning by providing uncorrelated transitions for training.
Time series forecasting is crucial in fields like finance and meteorology because it helps in predicting stock prices and ________ respectively.
- Temperature
- Rainfall
- Crop yields
- Wind speed
Time series forecasting in meteorology is important for predicting variables like rainfall, not stock prices.
In the context of Q-learning, what does the 'Q' stand for?
- Quality
- Quantity
- Question
- Quotient
In Q-learning, the 'Q' stands for Quality, representing the quality or expected return of taking a specific action in a given state.