True or false: Traditionally, XGBoost is slower than lightGBM but it achieves faster training through the Histogram binning process.1 pointTrueFalse

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.

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.

9. A Machine Learning Specialist has created a hyperparameter tuning job a notebook instance. The tuning job will use the XGBoost algorithm to train a classification model. The ML Specialist wants to visualize the correlation of the eta, alpha, max_depth, and min_child_weight hyperparameters with the model’s performance at each iteration so she can reconfigure them to attain the best model version. In doing so, the time and cost it takes to train the model will decrease. Which visualization technique should the ML Specialist use?Use a scatter plot with data points colored by the AUC metric and apply t-Distributed Stochastic Neighbor Embedding (t-SNE) to the input variables to generate better data visualizations.Use a scatter plot to visualize the results for each root mean square error (RMSE)-hyperparameter combination.Use a scatter plot to visualize the results for each Area Under the Curve (AUC)-hyperparameter combination.Use a histogram to visualize the results and only reconfigure hyperparameters near the mean for subsequent iterations.

5. A company serves a free-to-play online game with over a million active users. The game profits by inducing players to spend money on loot boxes. A Machine Learning Specialist uses data from 500,000 random users to train an XGBoost model that predicts players who are likely to buy at least 5 boxes within a month based on age, gender, playing hours, engagement patterns, etc. The collected data contains 150,000 positive samples and 350,000 negative samples. The model has high accuracy on the training dataset but low on the test dataset. The company wants to maximize profit by capturing as many players who are likely to buy as possible through targeted promotions. Which methods could the Specialist do to rectify the problem?Choose random samples of the training data and copy them to the test data.Tweak the cost function to give more weight to false negatives than false positives.Copy a subset of the positive samples and add noise to the copied data.Increase the maximum depth of a tree.Tweak the cost function to give more weight to false positives than false negatives

If we build m trees in XGBoost, how many trees will work on bootstrapped data?Answer choicesSelect an option1M-1M0