Technical Interview Questions
Receiver operating characteristics (ROC curve): ROC curve illustrates the diagnostic ability of a binary classifier. It is calculated/created by plotting True Positive against False Positive at various threshold settings. The performance metric of ROC curve is AUC (area under curve). Higher the area under the curve, better the prediction power of the model. In the … Read more
Although it depends on the problem you are solving, but some general advantages are following: Naive Bayes: Work well with small dataset compared to DT which need more data Lesser overfitting Smaller in size and faster in processing Decision Trees: Decision Trees are very flexible, easy to understand, and easy to debug No preprocessing or … Read more
L2 regularization: It tries to spread error among all the terms. L2 corresponds to a Gaussian prior. L1 regularization: It is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms. L1 and L2 regularization are techniques used in machine … Read more
Type I and Type II error in machine learning refers to false values. Type I is equivalent to a False positive while Type II is equivalent to a False negative. In Type I error, a hypothesis which ought to be accepted doesn’t get accepted. Similarly, for Type II error, the hypothesis gets rejected which should … Read more
Adjusted R2 because the performance of predictors impacts it. R2 is independent of predictors and shows performance improvement through increase if the number of predictors is increased. The choice between R2 (coefficient of determination) and adjusted R2 depends on the specific context of the model evaluation. R2 (Coefficient of Determination): This metric measures the proportion … Read more