Answer / Mamta Rani

A Logistic Regression Model can be evaluated using metrics like:
- Accuracy (ratio of correct predictions to the total number of instances)
- Confusion Matrix (a table that shows the number of true positives, false positives, true negatives, and false negatives)
- Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
- Precision (the ratio of true positives to the sum of true positives and false positives)
- Recall or Sensitivity (the ratio of true positives to the sum of true positives and false negatives)

Name some feature extraction techniques used for dimensionality reduction?

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Tell us what's the difference between a generative and discriminative model?

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What is feature scaling?

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Do you think that treating a categorical variable as a continuous variable would result in a better predictive model?

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You are given a dataset where the number of variables (p) is greater than the number of observations (n) (p>n). Which is the best technique to use and why ?

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What are the last machine learning papers you've read?

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What is A/B testing in Machine Learning?

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How would you screen for outliers and what should you do if you find one?

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Explain me how would you handle an imbalanced dataset?

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Give a popular application of machine learning that you see on day to day basis?

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Why is python best for machine learning?

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Comparision between Machine Learning and Big Data?

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