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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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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.
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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.
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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.
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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.
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In convolutional neural networks, using weights from models trained on large datasets like ImageNet as a starting point for training on a new task is an application of ________.

  • Transfer Learning
  • Regularization
  • Batch Normalization
  • Data Augmentation
This application of transfer learning involves using pre-trained CNN models, like those on ImageNet, to initialize weights in a new model for a different task. It accelerates training and leverages existing knowledge.
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While LSTMs have three gates, the GRU simplifies the model by using only ________ gates.

  • 1
  • 2
  • 3
  • 4
Gated Recurrent Units (GRUs) simplify the model by using only two gates: an update gate and a reset gate, as opposed to the three gates in LSTMs.
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In a situation where you have both numerical and categorical data, which clustering method might pose challenges, and why?

  • Agglomerative Clustering
  • DBSCAN Clustering
  • Hierarchical Clustering
  • K-Means Clustering
K-Means may pose challenges in such a situation because it calculates centroids using the mean, which isn't well-defined for categorical data. Other methods like hierarchical or DBSCAN may be more suitable.
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An online retailer wants to create a hierarchical structure of product categories based on product descriptions and features. They want this hierarchy to be easily interpretable and visual. Which clustering approach would be most suitable?

  • Hierarchical Clustering
  • DBSCAN
  • Gaussian Mixture Model (GMM)
  • Affinity Propagation
For creating a hierarchical structure, Hierarchical Clustering is the most suitable approach. It builds a tree-like structure that is interpretable and can be easily visualized. This makes it ideal for organizing product categories based on descriptions and features.
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