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.
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.
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.
Dimensionality reduction techniques, like PCA and t-SNE, are essential when dealing with the ________ curse.
- Overfitting
- Bias-Variance Tradeoff
- Curse of Dimensionality
- Bias
The "Curse of Dimensionality" refers to the increased complexity and sparsity of data in high-dimensional spaces. Dimensionality reduction techniques, such as PCA (Principal Component Analysis) and t-SNE, are crucial to mitigate the adverse effects of this curse.
Consider a robot that learns to navigate a maze. Instead of learning the value of each state or action, it tries to optimize its actions based on direct feedback. This approach is most similar to which reinforcement learning method?
- Monte Carlo Methods
- Temporal Difference Learning (TD)
- Actor-Critic Method
- Q-Learning
In this context, the robot is optimizing actions based on direct feedback, which is a characteristic of the Actor-Critic method. This method combines value-based and policy-based approaches, making it similar to the situation described.
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.