One of the common algorithms used to solve the multi-armed bandit problem is the ________ algorithm.
- UCB (Upper Confidence Bound)
- Q-Learning
- A* (A-Star)
- K-Means
The Upper Confidence Bound (UCB) algorithm is a common approach to solving the multi-armed bandit problem, providing a balance between exploration and exploitation.
Why is balancing exploration and exploitation crucial in reinforcement learning?
- To optimize the learning process
- To simplify the problem
- To minimize the rewards
- To increase computational efficiency
Balancing exploration and exploitation is crucial because it helps the agent learn the environment without getting stuck in suboptimal actions.
Which of the following describes the situation when a model performs well on the training data but poorly on unseen data?
- Bias
- High Variance
- Overfitting
- Underfitting
This situation is known as overfitting, where a model learns to fit the training data too closely but fails to generalize to new, unseen data, resulting in a high error rate.
One of the challenges in DQN is that small updates to Q values can lead to significant changes in the policy, making the learning process highly ________.
- Sensitive
- Efficient
- Predictable
- Robust
The term 'sensitive' in this context refers to the fact that small changes in Q values can have a disproportionate impact on the policy, making it unstable and hard to control.
The multi-armed bandit problem can be viewed as a simplified version of the reinforcement learning problem where the number of ________ is just one.
- Episodes
- States
- Actions
- Rewards
The multi-armed bandit problem simplifies reinforcement learning to just one action, where you need to decide which arm of a bandit to pull.
In a video game with multiple levels and complex interactions, what approach is suitable for training an AI agent optimally?
- Transfer Learning
- Curriculum Learning
- Random Search
- Supervised Learning
Curriculum Learning is ideal for training an AI agent to handle various levels with different challenges. It starts with easy levels, gradually increasing difficulty based on the agent's performance in earlier stages, ensuring effective learning.
Random Forests introduce randomness in two main ways: by bootstrapping the data and by selecting a random subset of ______ for every split.
- Data Points
- Features
- Leaves
- Trees
Random Forests introduce randomness by selecting a random subset of "Features" for every split in each tree. This helps in creating diverse trees, which collectively improve the overall performance and reduce the risk of overfitting.
The ability of an individual or a group to understand and trust the model's decisions is often tied to the model's ________.
- Explainability
- Complexity
- Accuracy
- Processing speed
Model explainability is essential for understanding and trusting a model's decisions, especially in critical applications like healthcare or finance, where transparency is key for decision-making and accountability.
Which machine learning algorithm is commonly used for time series forecasting due to its ability to remember long sequences?
- Decision Trees.
- Recurrent Neural Networks (RNNs).
- Support Vector Machines (SVMs).
- K-Means Clustering.
Recurrent Neural Networks (RNNs) are favored for time series forecasting because they can remember and model long sequences of data, making them suitable for sequential data like time series.
When dealing with high-dimensional data, which of the two algorithms (k-NN or Naive Bayes) is likely to be more efficient in terms of computational time?
- Both Equally Efficient
- It depends on the dataset size
- Naive Bayes
- k-NN
Naive Bayes is generally more efficient in terms of computational time for high-dimensional data because it doesn't require distance calculations.