K-Fold Cross-Validation and Model Selection
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Questions and Answers

What is the primary goal of problem formulation in machine learning?

  • To collect and manage data for the problem
  • To define the problem, input, output, and loss function (correct)
  • To select the most suitable model for the problem
  • To evaluate the performance of the model
  • What technique can be used to handle unbalanced class data?

  • Feature engineering
  • Data augmentation
  • Model selection
  • Undersample the majority and over-sample the minority (correct)
  • What is the purpose of exploratory data analysis (EDA) in machine learning?

  • To select the most suitable model for the problem
  • To evaluate the performance of the model
  • To understand the distribution of the data and identify patterns (correct)
  • To handle missing data
  • What is the purpose of the receiver operating characteristic (ROC) curve in machine learning?

    <p>To evaluate the performance of the model</p> Signup and view all the answers

    What is the primary goal of model monitoring and maintenance in machine learning?

    <p>To monitor the model's performance on live data and handle nonstationarity</p> Signup and view all the answers

    What is the primary goal of the learning agent in model selection?

    <p>To minimize the loss function</p> Signup and view all the answers

    In k-fold cross-validation, what percentage of the data is held out as a validation set in each round?

    <p>1/k</p> Signup and view all the answers

    What is the purpose of the validation set in k-fold cross-validation?

    <p>To evaluate the model</p> Signup and view all the answers

    Which of the following is a consideration in model selection and optimization?

    <p>Minimizing the loss function</p> Signup and view all the answers

    What is the purpose of using k-fold cross-validation with different values of k?

    <p>To select the optimal value of k</p> Signup and view all the answers

    What is the relationship between the complexity of the model and the error rate on the training data?

    <p>The error rate decreases as the complexity of the model increases</p> Signup and view all the answers

    What is a major drawback of decision trees?

    <p>They are unstable and can change significantly with the addition of a single example</p> Signup and view all the answers

    What is the purpose of the validation set in model selection?

    <p>To evaluate and choose the best candidate model</p> Signup and view all the answers

    What is the learning curve for a decision tree learning algorithm?

    <p>A graph showing the relationship between the number of training examples and the average error rate</p> Signup and view all the answers

    What is the error rate of a hypothesis?

    <p>The proportion of times that h(x) ≠ y for a sample (x, y)</p> Signup and view all the answers

    How can decision trees be made more widely useful?

    <p>By handling missing data, continuous and multivalued input attributes, and continuous-valued output attributes</p> Signup and view all the answers

    What is the purpose of model selection?

    <p>To choose a good hypothesis space</p> Signup and view all the answers

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