Technical Interview Questions
First reason is that XGBoos is an ensemble method that uses many trees to make a decision so it gains power by repeating itself. SVM is a linear separator, when data is not linearly separable SVM needs a Kernel to project the data into a space where it can separate it, there lies its greatest … Read more
SVM algorithms have basically advantages in terms of complexity. First I would like to clear that both Logistic regression as well as SVM can form non linear decision surfaces and can be coupled with the kernel trick. If Logistic regression can be coupled with kernel then why use SVM? ● SVM is found to have … Read more
Model Evaluation is a very important part in any analysis to answer the following questions, How well does the model fit the data?, Which predictors are most important?, Are the predictions accurate? So the following are the criterion to access the model performance, 1. Akaike Information Criteria (AIC): In simple terms, AIC estimates the relative … Read more
It is the sum of the likelihood residuals. At record level, the natural log of the error (residual) is calculated for each record, multiplied by minus one, and those values are totaled. That total is then used as the basis for deviance (2 x ll) and likelihood (exp(ll)). The same calculation can be applied to … Read more
AUC (area under curve). Higher the area under the curve, better the prediction power of the model. In the context of machine learning, the AUC (Area Under the Curve) refers to the area under the Receiver Operating Characteristic (ROC) curve. The ROC curve is a graphical representation that illustrates the performance of a binary classification … Read more