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Intro to ML - Quiz

This quiz is just to revise the concepts covered in the session

Change the markdown file for submission of the quiz

image

  • Suppose you are working on weather prediction and use a learning algorithm to predict tomorrow's temperature . What kind of problem would that be?

    • Classification
    • Regression

  • Suppose that you have trained a logistic regression classifier, and it outputs on a new example x a prediction hθ​ (x) = 0.4. This means (check all that apply):

    • Our estimate for P(y = 0| x,θ) = 0.6
    • Our estimate for P(y = 1| x,θ) = 0.4
    • Our estimate for P(y = 1| x,θ) = 0.6
    • Our estimate for P(y = 0| x,θ) = 0.4

  • Which of the following are reasons for using feature scaling?

    • It speeds up gradient descent by making it require fewer iterations to get to a good solution.
    • It speeds up solving for θ using the normal equation.
    • It prevents the matrix XTX (used in the normal equation) from being non-invertable (singular/degenerate).
    • It is necessary to prevent gradient descent from getting stuck in local optima.

  • Which of the following statements are true? Check all that apply.

    • The cost function J(θ) for logistic regression trained with m≥1 examples is always greater than or equal to zero.
    • The sigmoid function g(z)=1/1+e^−z is never greater than one (>1).
    • For logistic regression, sometimes gradient descent will converge to a local minimum (and fail to find the global minimum).
    • Linear regression always works well for classification if you classify by using a threshold on the prediction made by linear regression.

  • KNN algorithm does more computation on test time rather than train time.

    • True
    • False

  • Which of the following distance metric can not be used in KNN?

    • Manhattan
    • Minkowski
    • Euclidean
    • All of them can be used

  • Which of the following machine learning algorithm can be used for imputing missing values of both categorical and continuous variables?

    • KNN
    • Logistic Regression
    • Linear Regression

  • Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable. You want to predict the class of new data point x=1 and y=1 using eucledian distance in 3-NN. In which class this data point belong to?

    image

    • + class
    • - class
    • Can't Say