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.
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.
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.
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.
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.
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.
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.
The process of adding a penalty to the loss function to discourage complex models is called ________.
- Normalization
- Optimization
- Parameterization
- Regularization
Regularization is a technique used in machine learning to add a penalty to the loss function, discouraging overly complex models and preventing overfitting. It helps improve a model's generalization to new data.
What is the central idea behind using autoencoders for anomaly detection in data?
- Autoencoders learn a compressed data representation
- Autoencoders are trained on anomalies
- Autoencoders are rule-based
- Autoencoders use labeled data
Autoencoders for anomaly detection learn a compressed representation of normal data, and anomalies can be detected when the reconstruction error is high.