Important Questions for Data Scientist Interview Pt-2

Q.?How do you generally choose among different classification models to decide which one is performing the best??

A. Here are some important considerations while choosing an algorithm:?Size of the training data, Accuracy and/or Interpretability of the output, Speed or Training time, Linearity and number of features.???

Q. How do you perform feature selection?

?A. Unsupervised: Do not use the target variable (e.g. remove redundant variables). Correlation.??Supervised: Use the target variable (e.g. remove irrelevant variables). Wrapper: Search for well-performing subsets of features. RFE.????

Q. What is an intercept in a Linear Regression? What is its significance??

A. The intercept (often labeled as constant) is the point where the function crosses the y-axis. In some analysis, the regression model only becomes significant when we remove the intercept, and the regression line reduces to Y = b*X + error.?The intercept (often labeled the constant) is the expected mean value of Y when all X="0. Start with a regression equation with one predictor, X. If X sometimes equals 0, the intercept is simply the expected mean value of Y at that value. If X never equals 0, then the intercept has no intrinsic meaning.

Q.?Filter and wrapper feature selection methods?

A. Wrapper methods measure the “usefulness” of features based on the classifier performance. In contrast, the filter methods pick up the intrinsic properties of the features (i.e., the “relevance” of the features) measured via univariate statistics instead of cross-validation performance. So, wrapper methods are essentially solving the “real” problem (optimizing the classifier performance), but they are also computationally more expensive compared to filter methods due to the repeated learning steps and cross-validation.

Filter methods: information gain, chi-square test, fisher score, correlation coefficient, variance threshold

Wrapper methods: recursive feature elimination, sequential feature selection algorithms, genetic algorithms

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