Interview Questions on Machine Learning

You have built a multiple regression model. Your model R² isn’t as good as you wanted. For improvement, your remove the intercept term, your model R² becomes 0.8 from 0.3. Is it possible? How?

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Yes, it is possible. We need to understand the significance of intercept term in a regression model. The intercept term shows model prediction without any independent variable i.e. mean prediction. The formula of R² = 1 – ∑(y – y´)²/∑(y – ymean)² where y´ is predicted value. When intercept term is present, R² value evaluates … Read more

How is True Positive Rate and Recall related? Write the equation

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True Positive Rate = Recall. Yes, they are equal having the formula (TP/TP + FN). The True Positive Rate (TPR), also known as Sensitivity or Recall, measures the proportion of actual positives that are correctly identified by a classifier. Mathematically, it is calculated as the ratio of True Positives (TP) to the sum of True … Read more

How is kNN different from kmeans clustering?

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Don’t get mislead by ‘k’ in their names. You should know that the fundamental difference between both these algorithms is, kmeans is unsupervised in nature and kNN is supervised in nature. kmeans is a clustering algorithm. kNN is a classification (or regression) algorithm. kmeans algorithm partitions a data set into clusters such that a cluster … Read more

After spending several hours, you are now anxious to build a high accuracy model. As a result, you build 5 GBM models, thinking a boosting algorithm would do the magic. Unfortunately, neither of models could perform better than benchmark score. Finally, you decided to combine those models. Though, ensembled models are known to return high accuracy, but you are unfortunate. Where did you miss?

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As we know, ensemble learners are based on the idea of combining weak learners to create strong learners. But, these learners provide superior result when the combined models are uncorrelated. Since, we have used 5 GBM models and got no accuracy improvement, suggests that the models are correlated. The problem with correlated models is, all … Read more

You are given a data set. The data set contains many variables, some of which are highly correlated and you know about it. Your manager has asked you to run PCA. Would you remove correlated variables first? Why?

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Chances are, you might be tempted to say No, but that would be incorrect. Discarding correlated variables have a substantial effect on PCA because, in presence of correlated variables, the variance explained by a particular component gets inflated. For example: You have 3 variables in a data set, of which 2 are correlated. If you … Read more