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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 16

Q1 | Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
Q2 | Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
Q3 | Which of the one is true about Heteroskedasticity?
Q4 | Which of the following assumptions do we make while deriving linear regression parameters?1. The true relationship between dependent y and predictor x is linear2. The model errors are statistically independent3. The errors are normally distributed with a 0 mean and constant standard deviation4. The predictor x is non-stochastic and is measured error-free
Q5 | To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?
Q6 | which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.
Q7 | Can we calculate the skewness of variables based on mean and median?
Q8 | Which of the following is true aboutRidge or Lasso regression methods in case of feature selection?
Q9 | Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R- Squared increases and Adjusted R-
Q10 | How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?
Q11 | Conditional probability is a measure of the probability of an event given that another event has already occurred.
Q12 | What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function
Q13 | Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very small C (C~0)?
Q14 | The cost parameter in the SVM means:
Q15 | If you remove the non-red circled points from the data, the decision boundary will
Q16 | The SVMs are less effective when:
Q17 | If there is only a discrete number of possible outcomes called          .
Q18 | Some people are using the term        instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
Q19 | The term           can be freely used, but with the same meaning adopted in physics or system theory.
Q20 | Common deep learning applications / problems can also be solved using        
Q21 | Identify the various approaches for machine learning.
Q22 | what is the function of Unsupervised Learning?