Introduction to Machine Learning Landscape FALL 2020
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Questions and Answers

What is the purpose of unsupervised learning clustering?

  • To estimate the probability that an instance belongs to a particular class
  • To predict a continuous output variable
  • To impose a small penalty on model complexity
  • To divide data into distinct groups based on similarities (correct)
  • In the k-means algorithm, what is done in the E-Step?

  • Assigning points to the nearest cluster center (correct)
  • Guessing some cluster centers
  • Setting the cluster centers to the mean
  • Calculating the slope of the cost function
  • What is used to ensure that a model is not overfitting to the training data?

  • Estimating the probability that an instance belongs to a particular class
  • Regularization (correct)
  • Imposing a small penalty on model complexity
  • Setting the cluster centers to the mean
  • What is the definition of machine learning according to Arthur Samuel in 1959?

    <p>A field of study that gives computers the ability to learn without being explicitly programmed</p> Signup and view all the answers

    In which type of machine learning system is the training data labeled with desired solutions or labels?

    <p>Supervised Learning</p> Signup and view all the answers

    What type of machine learning system uses training data that is unlabeled?

    <p>Unsupervised Learning</p> Signup and view all the answers

    What is the main advantage of using a Decision Tree?

    <p>It is resistant to overfitting</p> Signup and view all the answers

    How does Lasso Regression differ from Ridge Regression?

    <p>Lasso Regression minimizes mean squared error (MSE), while Ridge Regression minimizes impurity</p> Signup and view all the answers

    What is the main idea behind Ensemble Methods like Random Forest?

    <p>To combine the predictions of multiple decision trees to reduce overfitting</p> Signup and view all the answers

    What is the key concept behind Gradient Boosting?

    <p>Trying to fit a new predictor to the residual errors made by the previous predictor</p> Signup and view all the answers

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