AzureML - Zero to Hero

The presentation for SQLUG meetup.

Transcript

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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
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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)
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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
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