5 MUST KNOW QUESTIONS FOR A DATA SCIENTIST

Q.? What is SelectK best? how does it works??

A. The SelectKBest method selects the features according to the k highest score. By changing the 'score_func' parameter we can apply the method for both classification and regression data. Selecting best features is important process when we prepare a large dataset for training. The SelectKBest class just scores the features using a function (in this case f_classif but could be others) and then "removes all but the k highest scoring features".?

Q. Why is mean square error a bad measure of model performance???

A.? Mean Squared Error (MSE) gives a relatively high weight to large errors — therefore, MSE tends to put too much emphasis on large deviations.?

?Q.? Why use Decision Trees??

A. First, a decision tree is a visual representation of a decision situation (and hence aids communication). Second, the branches of a tree explicitly show all those factors within the analysis that are considered relevant to the decision (and implicitly those that are not).?

?Q. R2 vs Adjusted R2? During Model Development which one do we consider???

A. R2 is statistical measurement used to explain the dependent and independent variables.? Adjusted R Squared is a measurement that predicts the regression variables. This model will take additional input variable that predicts to solve the problems. Adjusted R2 is the better model when you compare models that have a different amount of variables.?

?Q.? What is Yolo??

A. YOLO - You Only Look Once is an algorithm proposed by by Redmond et. al in a research article published at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) as a conference paper, winning OpenCV People’s Choice Award.?Compared to the approach taken by object detection algorithms before YOLO, which repurpose classifiers to perform detection, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.?

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