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In the Actor-Critic model, what role does the Critic's feedback play in adjusting the Actor's policies?

  • Evaluating policy
  • Selecting actions
  • Providing rewards
  • Discovering optimal actions
The Critic in the Actor-Critic model evaluates the current policy by estimating the value function. This evaluation helps the Actor make better decisions by guiding it towards actions that result in higher expected rewards, ultimately improving the policy.
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An online retailer wants to recommend products to users. They have a vast inventory, and they're unsure which products are most likely to be purchased. Every time a product is recommended and purchased, the retailer gets a reward. This setup is reminiscent of which problem?

  • Recommender Systems
  • NLP for Sentiment Analysis
  • Clustering and Dimensionality Reduction
  • Reinforcement Learning
The retailer's challenge of recommending products and receiving rewards upon purchase aligns with Recommender Systems. In this problem, algorithms are used to predict user preferences and recommend items to maximize user satisfaction and sales.
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If you want to predict whether an email is spam (1) or not spam (0), which regression technique would you use?

  • Decision Tree Regression
  • Linear Regression
  • Logistic Regression
  • Polynomial Regression
For this classification task (spam or not spam), Logistic Regression is appropriate. It models the probability of the email being spam and maps it to a binary outcome.
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The value at which the sigmoid function outputs a 0.5 probability, thereby determining the decision boundary in logistic regression, is known as the ________.

  • Decision Point
  • Inflection Point
  • Sigmoid Threshold
  • Threshold Value
The value at which the sigmoid function outputs a 0.5 probability is known as the decision point. This is the threshold value that separates the two classes in a binary logistic regression.
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In which learning approach does the model learn to...

  • Reinforcement Learning
  • Semi-Supervised Learning
  • Supervised Learning
  • Unsupervised Learning
In reinforcement learning, a model learns by interacting with an environment and receiving rewards or penalties based on its actions. It aims to make decisions to maximize cumulative rewards.
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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.
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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.
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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.
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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.
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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.
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