AzureML - Zero to Hero

The presentation for SQLUG meetup.

Transcript

9. AzureML - where experiments are done and deployed as web services AzureML studio has “toolbar” which has modules for data ingestion/transformation, statistics, machine learning. Some of them have properties which can be set. AzureML has Datasets which can be bought in at runtime or persisted inside. It has public datasets too. AzureML 9
11. Classification algorithms can be measured by these metrics Regression have just RMSE which many people are questioning in present circumstances (Sum through all instances (actual class value - predicted one)) Clustering has different mechanism and requires tests/re-runs to ensure grouped/clustered points have cohesion of somekind Types of classification errors often incur different costs. Total error = (FP+FN)/(TP+FP+TN+FN) Lift charts Sort instances by their predicted probability of being a true positive (TP). X axis is sample size and Y axis is number of true positives (TP). ROC curves (ROC means receiver operating characteristic, a term from signal processing) X axis shows %of false positives (FP) Y axis shows %of true positives (TP). Recall - precision (IR world- search world has these terms too ): Precision (retrieved relevant / total retrieved) = TP / (TP+FP) Recall (retrieved relevant / total relevant) = TP / (TP + FN) 11
14. Desirables Ipython like “executable” documented – DS – how to achieve in simple way Model interpretation More visualization HMM Native Time series Text analysis – IR integration 14