1) Model building process of one of my previous projects 2) Random forest hyperparameters 3) ROC curve, using the ROC curve to set probability cutoffs in classication models 4) Gradient boosting techniques like XGBoost and their hyperparameters 5) Guesstimation question: How would I ascertain the value of a real estate property

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Data Scientist interview questions on model building, random forest, ROC curve, gradient boosting, and real estate valuation

For model building, I followed the CRISP-DM process and used various algorithms like logistic regression, decision trees, and random forest
Random forest hyperparameters include number of trees, maximum depth, minimum samples split, and minimum samples leaf
ROC curve is a graphical representation of the trade-off between true positive rate and false positive rate. Probability cutoffs can be set based on the desired balance between sensitivity and specificity
Gradient boosting techniques like XGBoost use boosting to combine weak learners and improve model performance. Hyperparameters include learning rate, number of trees, and maximum depth
Real estate valuation can be done using various methods like comparable sales approach, income approach, and cost approach

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