A company wants to determine the best version of their website homepage among five different designs. They decide to show each version to a subset of visitors and observe which version results in the highest user engagement. This problem is analogous to which classical problem in reinforcement learning?
- Multi-Armed Bandit
- Q-Learning
- Deep Q-Network (DQN)
- Policy Gradient Methods
This scenario is analogous to the Multi-Armed Bandit problem, where a decision-maker must choose between multiple options to maximize cumulative reward, akin to selecting the best website version for maximum user engagement.
You're analyzing data from a shopping mall's customer behavior and notice that there are overlapping clusters representing different shopping patterns. To model this scenario, which algorithm would be most suitable?
- K-Means Clustering
- Decision Trees
- Breadth-First Search
- Radix Sort
K-Means Clustering is commonly used for clustering tasks, such as identifying distinct shopping patterns. It groups data into clusters based on similarity, making it suitable for analyzing customer behavior data with overlapping patterns.
Hierarchical clustering can be broadly classified into two types based on how the hierarchy is constructed. What are these two types?
- Agglomerative and Divisive
- Linear and Non-linear
- QuickSort and MergeSort
- Recursive and Iterative
Hierarchical clustering can be Agglomerative (bottom-up) or Divisive (top-down) based on how clusters are merged or divided in the hierarchy.
When a machine learning algorithm tries to group data into clusters without prior labels, it is using which type of learning?
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
Unsupervised Learning is the technique where the algorithm groups data into clusters without prior labels. This can help identify patterns and relationships within the data.
Which term refers to the error introduced by the tendency of a model to fit the training data too closely, capturing noise?
- Bias
- Overfitting
- Underfitting
- Variance
Overfitting is the term used to describe when a model fits the training data too closely, capturing noise and leading to poor generalization on unseen data. It results in a high variance.
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.
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.
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.
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.
One of the hyperparameters in a Random Forest algorithm that determines the maximum depth of the trees is called ______.
- Entropy
- Gini Index
- LeafNodes
- MaxDepth
The hyperparameter controlling the maximum depth of trees in a Random Forest is typically called "MaxDepth." It determines how deep each decision tree can grow in the ensemble.
In reinforcement learning, ________ focuses on trying new actions, while ________ focuses on leveraging known rewards.
- Exploration Policy
- Exploitation Policy
- Random Policy
- Deterministic Policy
In reinforcement learning, exploration policy focuses on trying new actions to learn more about the environment. Exploitation policy, on the other hand, leverages known rewards to make optimal decisions based on what's already learned.
In reinforcement learning, what do we call the function that determines the value of taking an action in a particular state?
- Action Evaluator
- Value Function
- Policy Function
- Reward Function
The 'Value Function' in reinforcement learning determines the expected cumulative reward of taking an action in a particular state, guiding decision-making.