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Machine Learning

Linear Methods For Classification

Interview questions on Linear Methods For Classification.

8 questions

All subtopicsGeneral ConceptsOdds and Log-oddsThe SigmoidTruly Understanding Logistic RegressionThe Logit Function and Entropy

General Concepts

Q1. **True or False**: For a fixed number of observations in a data set, introducing more variables normally generates a model that has a better fit to the data. What may be the drawback of such a model-fitting strategy?

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General Concepts

Q2. Define the term **odds of success** both qualitatively and formally. Give a numerical example that stresses the relation between probability and odds of an event occurring.

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General Concepts

Q3. Answer the following:

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  1. Define what is meant by the term "interaction", in the context of a logistic regression predictor variable.
  2. What is the simplest form of an interaction? Write its formulae.
  3. What statistical tests can be used to attest to the significance of an interaction term?

General Concepts

Q4. **True or False**: In machine learning terminology, unsupervised learning refers to the mapping of input covariates to a target response variable that is attempted to be predicted when the labels are known.

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General Concepts

Q5. **Complete the following sentence**: In the case of logistic regression, the response variable is the log of the odds of being classified in `[...]`.

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General Concepts

Q6. Describe how in a logistic regression model, a transformation to the response variable is applied to yield a probability distribution. Why is it considered a more informative representation of the response?

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General Concepts

Q7. Complete the following sentence: Minimizing the negative log-likelihood also means

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maximizing the [...] of selecting the [...] class.

General Concepts

Q8. Assume the probability of an event occurring is `p = 0.1`.

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  1. What are the odds of the event occurring?
  2. What are the log odds of the event occurring?
  3. Construct the probability of the event as a ratio that equals 0.1