Which of the given options is not associated with XGBoost?Select an option Clear ResponseIt has an in-built routine for the purpose of handling the missing values.It has lower predictive power than Gradient Boosting.It helps in reducing overfit.It implements parallel processing technique.
Question
Which of the given options is not associated with XGBoost?Select an option Clear ResponseIt has an in-built routine for the purpose of handling the missing values.It has lower predictive power than Gradient Boosting.It helps in reducing overfit.It implements parallel processing technique.
Solution
The option that is not associated with XGBoost is "It has lower predictive power than Gradient Boosting."
XGBoost, short for Extreme Gradient Boosting, is actually known for its high predictive power. It is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. It implements machine learning algorithms under the Gradient Boosting framework, and it provides a parallel tree boosting to solve many data science problems in a fast and accurate way. Therefore, saying it has lower predictive power than Gradient Boosting is incorrect.
Similar Questions
Gradient Boosting Framework: XGBoost operates within a gradient boosting framework, where a sequence of weak learners (decision trees) are trained iteratively to correct the errors made by the preceding models. This iterative process allows XGBoost to gradually improve its predictive accuracy by learning from the mistakes of earlier models.
True or false: Traditionally, XGBoost is slower than lightGBM but it achieves faster training through the Histogram binning process.1 pointTrueFalse
Which of the following is NOT a recommended way of dealing with missing values:Group of answer choicesUse a model with predicts the missing value from the other fieldsRemove the whole column if there are missing values in some row of that columnPut a NULL where there is a missing valueRemove the whole row if there are missing values in some column of that row
Which of the following is NOT a recommended way of dealing with missing values:Group of answer choicesPut a NULL where there is a missing valueRemove the whole column if there are missing values in some row of that columnUse a model with predicts the missing value from the other fieldsRemove the whole row if there are missing values in some column of that row
Missing responses represent values of a variable that are unknown, either because respondents provided ambiguous answers or their answers were not properly recordedSelect one:TrueFalse
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