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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?
- since the there is a relationship means our model is not good
- since the there is a relationship means our model is good
- cant say
- none of these
Q2 | Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
- you will always have test error zero
- you can not have test error zero
- none of the above
Q3 | Which of the one is true about Heteroskedasticity?
- linear regression with varying error terms
- linear regression with constant error terms
- linear regression with zero error terms
- none of these
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
- 1,2 and 3.
- 1,3 and 4.
- 1 and 3.
- all of above.
Q5 | To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?
- scatter plot
- barchart
- histograms
- none of these
Q6 | which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.
- the polynomial degree
- whether we learn the weights by matrix inversion or gradient descent
- the use of a constant-term
Q7 | Can we calculate the skewness of variables based on mean and median?
- true
- false
Q8 | Which of the following is true aboutRidge or Lasso regression methods in case of feature selection?
- ridge regression uses subset selection of features
- lasso regression uses subset selection of features
- both use subset selection of features
- none of above
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-
- 1 and 2
- 1 and 3
- 2 and 4
- none of the above
Q10 | How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?
- 1
- 2
- cant say
Q11 | Conditional probability is a measure of the probability of an event given that another event has already occurred.
- true
- false
Q12 | What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function
- 1
- 2
- 1 and 2
- none of these
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)?
- misclassification would happen
- data will be correctly classified
- cant say
- none of these
Q14 | The cost parameter in the SVM means:
- the number of cross- validations to be made
- the kernel to be used
- the tradeoff between misclassification and simplicity of the model
- none of the above
Q15 | If you remove the non-red circled points from the data, the decision boundary will
- true
- false
Q16 | The SVMs are less effective when:
- the data is linearly separable
- the data is clean and ready to use
- the data is noisy and contains overlapping points
Q17 | If there is only a discrete number of possible outcomes called .
- modelfree
- categories
- prediction
- none of above
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.
- inference
- interference
- accuracy
- none of above
Q19 | The term can be freely used, but with the same meaning adopted in physics or system theory.
- accuracy
- cluster
- regression
- prediction
Q20 | Common deep learning applications / problems can also be solved using
- real-time visual object identification
- classic approaches
- automatic labeling
- bio-inspired adaptive systems
Q21 | Identify the various approaches for machine learning.
- concept vs classification learning
- symbolic vs statistical learning
- inductive vs analytical learning
- all above
Q22 | what is the function of Unsupervised Learning?
- find clusters of the data and find low-dimensional representations of the data
- find interesting directions in data and find novel observations/ database cleaning
- interesting coordinates and correlations
- all