GoranSMilovanovic added a comment.
Current status: - pilot/research experiments completed: - research phase: - model server response times from the features extracted as atomic elements of the SPARQL queries in the sample; - experimented with various feature selections (size of the feature vocabulary); - model: XGBoost for regression, RMSE optimization; - results: everything between approx. R = .72 (test data set) and R = .91 (train data set) can be achieved; - firs serious model: - goal: categorize unusually long server response times (> upper inner fence, Q3 + 1.5*IQR - "mild outliers"); - method: XGBoost optimization of logistic loss (i.e. say Binomial Regression from an ensemble of Decision Trees); - result: accuracy **92%** on both train and test data (approx. 50% split of 1M queries in the sample). NEXT steps: - running full CV cycles across learning rate, tree depth, taking best iterations in n-fold CVs only; - singling out the most reliable model; - attempt to predict extreme outliers (> upper inner fence, Q3 + 3*IQR - "extreme outliers"); - reporting until Wednesday, 2020/04/09; - clustering queries from the most important features in server response time optimization (if necessary - to discuss with the team). TASK DETAIL https://phabricator.wikimedia.org/T248308 EMAIL PREFERENCES https://phabricator.wikimedia.org/settings/panel/emailpreferences/ To: GoranSMilovanovic Cc: JAllemandou, Lucas_Werkmeister_WMDE, Simon_Villeneuve, dcausse, Jakob_WMDE, Gehel, Addshore, Lydia_Pintscher, WMDE-leszek, Aklapper, darthmon_wmde, Nandana, Lahi, Gq86, GoranSMilovanovic, QZanden, EBjune, merbst, LawExplorer, _jensen, rosalieper, Scott_WUaS, Jonas, Xmlizer, jkroll, Wikidata-bugs, Jdouglas, aude, Tobias1984, Manybubbles, Mbch331
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