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About Me
Mitch Wheat has been working as a professional programmer since 1984, graduating with a honours degree in Mathematics from Warwick University, UK in 1986. He moved to Perth in 1995, having worked in software houses in London and Rotterdam. He has worked in the areas of mining, electronics, research, defence, financial, GIS, telecommunications, engineering, and information management. Mitch has worked mainly with Microsoft technologies (since Windows version 3.0) but has also used UNIX. He holds the following Microsoft certifications: MCPD (Web and Windows) using C# and SQL Server MCITP (Admin and Developer). His preferred development environment is C#, .Net Framework and SQL Server. Mitch has worked as an independent consultant for the last 10 years, and is currently involved with helping teams improve their Software Development Life Cycle. His areas of special interest lie in performance tuning |
Tuesday, December 27, 2016R: Evaluating a classifier using standard performance evaluation metricsThe Azure ML team have released a useful Custom R Evaluator script for computing standard classifier performance metrics. The module expects as input a dataset containing the actual and predicted class labels (i.e. a confusion matrix). The R code is available at GitHub. Example output: $ConfusionMatrix Predicted Actual a b c a 27 2 5 b 1 24 2 c 1 5 33 $Metrics a b c Accuracy 0.8400000 0.8400000 0.8400000 Precision 0.9310345 0.7741935 0.8250000 Recall 0.7941176 0.8888889 0.8461538 F1 0.8571429 0.8275862 0.8354430 MacroAvgPrecision 0.8434093 0.8434093 0.8434093 MacroAvgRecall 0.8430535 0.8430535 0.8430535 MacroAvgF1 0.8400574 0.8400574 0.8400574 AvgAccuracy 0.8933333 0.8933333 0.8933333 MicroAvgPrecision 0.8400000 0.8400000 0.8400000 MicroAvgRecall 0.8400000 0.8400000 0.8400000 MicroAvgF1 0.8400000 0.8400000 0.8400000 MajorityClassAccuracy 0.3900000 0.3900000 0.3900000 MajorityClassPrecision 0.0000000 0.0000000 0.3900000 MajorityClassRecall 0.0000000 0.0000000 1.0000000 MajorityClassF1 0.0000000 0.0000000 0.5611511 Kappa 0.7581986 0.7581986 0.7581986 RandomGuessAccuracy 0.3333333 0.3333333 0.3333333 RandomGuessPrecision 0.3400000 0.2700000 0.3900000 RandomGuessRecall 0.3333333 0.3333333 0.3333333 RandomGuessF1 0.3366337 0.2983425 0.3594470 RandomWeightedGuessAccuracy 0.3406000 0.3406000 0.3406000 RandomWeightedGuessPrecision 0.3400000 0.2700000 0.3900000 RandomWeightedGuessRecall 0.3400000 0.2700000 0.3900000 RandomWeightedGuessF1 0.3400000 0.2700000 0.3900000 Wednesday, December 21, 2016Editions and Supported Features for SQL Server 2016Just posting this link so I can find it easily: Editions and Supported Features for SQL Server 2016 SQL Server 2016 SP1 onwards now supports Data Compression in Standard Edition (every edition in fact, including Express!) |
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