How to Identify Right Performance Evaluation Metrics In Machine Learning Based Dissertation - Phdassistance

1. HowtoIdentifyRightPerformance EvaluationMetricsInMachine LearningBasedDissertation AnAcademicpresentationby Dr.NancyAgnes,Head,TechnicalOperations,Phdassistance Group www.phdassistance.com Email:info@phdassistance.com

2. TODAY'SOUTLINE Introduction Regression metrics Classification metrics Conclusion Aboutphdassistance

3. INTRODUCTION Every Machine Learningpipeline has performance measurements. Theyinformyouifyou'reprogressingandgiveyou anumber. Ametricisrequiredforallmachinelearning models,whetherlinearregressionoraSOTA methodlike BERT.

4. EveryMachineLearningActivity,like performance measurements, can be split down into Regression or Classification. Forbothissues,therearehundredsofmetrics to choose from, but we'll go through the most commonones andtheinformationtheygive regardingmodelperformance. It'scritical tounderstandhowyourmodel interprets yourdata!

5. Lossfunctionsarenotthesameasmetrics.Lossfunctionsdisplayamodel's performance. They'reoftendifferentiableinthemodel'sparametersandareusedtotrainamachine learningmodel(usingsomeformofoptimizationlike GradientDescent). Metricsareusedtotrackandquantifyamodel'sperformance(duringtrainingand testing), and theydon'thave tobe differentiable. Iftheperformancemeasureisdifferentiableforsometasks,itmayalsobeutilizedasa lossfunction(possiblywithadditionalregularizations),suchasMSE.

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7. REGRESSIONMETRICS Theoutputofregressionmodelsiscontinuous. As a result, we'll need a measure that is based on computing some type of distance between anticipatedandactual values. We'llgothroughthesemachinelearning measuresindepthinordertoevaluate regressionmodels:

8. MEANABSOLUTEERROR(MAE) TheaverageofthedifferencebetweenthegroundtruthandprojectedvaluesistheMean AbsoluteError. There area few essential factorsforMAE to consider: Becauseitdoesnotexaggeratemistakes,itismoreresistanttooutliersthanMAE. It tells us how far the forecasts differed from the actual result. However, because MAE utilizes the absolute value of the residual, we won't know which way the mistake is going,i.e. whetherwe're under- orover-predicting thedata.

9. Thereisnoneedtosecond-guesserrorinterpretation. IncontrasttoMSE,whichisdifferentiable,MAEisnon-differentiable. Thismeasure,like MSE,is straightforwardtoapply. HirePhDAssistanceexpertstodevelopyouralgorithmandcodingimplementation onimprovingthe secureaccess foryourEngineeringdissertation Services.

10. MEAN SQUARED ERROR (MSE): Themeansquarederrorisarguablythemostoftenusedregressionstatistic.It simplycalculatestheaverageofthesquareddifferencebetweenthegoalvalue and the regressionmodel'sprojectedvalue. Afew essentialfeatures of MSE: Becauseitisdifferentiable,itcanbebetteroptimized.

11. Itpenalizesevenminormistakesbysquaringthem,resultinginanoverestimationofthe model's badness. Thesquaringfactor(scale)mustbeconsideredwhileinterpretingerrors. It'sindeedessentiallymorepronetooutliersthanothermeasuresduetothesquaringeffect. ROOTMEANSQUAREDERROR(RMSE) Thesquarerootoftheaverageofthesquareddifferencebetweenthetargetvalueandthe valuepredictedby theregressionmodelisthe RootMeanSquaredError. It corrects afew flaws in MSE.

12. AfewessentialpointsofRMSE: ItmaintainsMSE'sdifferentiablefeature. ItsquarerootsthepenalizationofminormistakesperformedbyMSE. Becausethescaleisnowthesameastherandomvariable,errorinterpretationissimple. Because scale factors are effectively standardized, outliers are less likely to cause problems. ItsapplicationissimilartoMSE.

13. PhDAssistanceexpertshasexperienceinhandlingDissertationAndAssignmentin cloud security and machine learning techniques with assured 2:1 distinction. Talk to ExpertsNow R²COEFFICIENTOFDETERMINATION The R2 coefficient of determination is a post measure, meaning it is determined after othermetrics have been calculated. The purpose of computing this coefficient is to answer the question "How much (what percentage) of the entire variance in Y (target) is explained by variation in X (regression line)?"Thesum of squared errors is usedto compute this.

14. AfewthoughtsontheR2results: If the regression line's sum of Squared Error is minimal, R2 will be near to 1 (ideal), indicating thatthe regression was able to capture100% of the variance in the targetvariable. Incontrast,iftheregressionline'ssumofsquarederrorishigh,R2willbecloseto0, indicatingthat the regression failedto capture any variation inthe target variable. The range of R2 appears to be (0,1), but it is really (-,1) since the ratio of squared errors of the regressionlineandmeanmightexceed 1ifthesquarederroroftheregressionlineis sufficientlyhigh (>squared error of the mean). PhD Assistancehas vast experience in developingdissertation research topicsfor students pursuingtheUK dissertationin business management.Order Now.

15. ADJUSTEDR² The R2 technique has various flaws, such as DeceivingThe Researcherinto assuming thatthemodelisimprovingwhenthescoreriseswhile,infact,nolearningistakingplace. This can occur when a model over fits the data; in such instance, the variance explained will be 100%, but no learning will have occurred. R2 is modified with the number of independentvariables to correct this. Adjusted R2 is usually lower than R2 since it accounts for rising predictors and only indicatesimprovement when there isone.

16. CLASSIFICATIONMETRICS Oneof the most explored fieldsinthe world isclassification issues.Almost all production and industrial contexts have use cases. The list goes on and on: speech recognition, facial recognition, textcategorization,andsoon. We need a measure that compares discrete classes in some way sinceclassificationalgorithmsprovidediscreteoutput.

17. Classification Metrics assess a model's performance and tell you if the classification is excellentor bad, buteach one doesit in a uniqueway. So,inordertoassessClassificationmodels,we'llgothroughthefollowingmeasuresin depth: ACCURACY Theeasiestmeasuretouseandapplyisclassificationaccuracy,whichisdefinedasthe numberofcorrectpredictionsdividedbythetotalnumberofpredictions,multipliedby100. Wemayaccomplishthismanuallyloopingbetweenthegroundtruthandprojectedvalues, orwe can use thescikit-learn module .

18. CONFUSIONMATRIX(NOTAMETRICBUTFUNDAMENTALTOOTHERS) TheGround-TruthLabelsvs.ModelPredictionsConfusionMatrix representation oftheground-truthlabelsvs.modelpredictions. is a tabular The examples in a predicted class are represented by each row of the confusion matrix, whereas theoccurrences inanactualclass arerepresented byeachcolumn.The Confusion Matrix isn't strictly a performance indicator, but it serves as a foundation for othermetrics to assess the outcomes. Weneedtoestablishavalueforthenullhypothesisasanassumptioninorderto comprehend the confusionmatrix.

19. 1.PRECISIONANDRECALL Type-I mistakes are the subject of the precision metric (FP). When we reject a valid null Hypothesis(H0), we make a Type-Imistake. For example, Type-I error mistakenly classifies cancer patients as non-cancerous. An accuracy score of 1 indicates that your model did not miss any true positives and can distinguishcorrectlybetweenaccurateandwrongcancerpatientlabeling. What itcan'tdetect isType-IIerror, orfalsenegatives,whichoccurwhen anon- cancerouspatient is mistakenlydiagnosed as malignant.

20. A low accuracy score (0.5) indicates that your classifier has a significant amount of false positives, which might be due to an imbalanced class or poorly adjusted model hyper parameters. The percentage of genuine positives to all positives in ground truth is known as the recall. The type-II mistake is the subject of the recall metric (FN). When we accept a false null hypothesis(H0), we make a type-IImistake. As a result, type-II mistake is mislabeling non-cancerous patients as malignant in this situation. Recalling to 1 indicates that your model did not miss any genuine positives and can distinguishproperlyfromwronglyclassifyingcancerpatients.

21. What it can't detect is type-I error, or false positives, which occur when a malignant patient is mistakenlydiagnosed asnon-cancerous. A low recallscore(0.5)indicates that yourclassifierhas a lot of falsenegatives, whichmight becaused byanunbalancedclass oranuntunedmodelhyper parameter. ToavoidFP/FNinanunbalancedclassissue,youmustprepareyourdataahead of timeusingover/under-sampling orfocalloss.

22. F1-SCORE Precision and recall are combined in the F1-score measure. In reality, the harmonic mean of the two is the F1 score. A high F1 score now denotes a high level of accuracy as well as recall. It has an excellent mix of precision and recall, and it performs well on tasks with unbalanced categorization. A low F1 score means (nearly) nothing; it merely indicates performance at a certain level.Wedidn'tstrivetoperformwellonalargeportionofthetestsetbecausewe had low recall. Low accuracy indicates that we didn't get many of the cases we recognised as affirmativecasesaccurate.

23. However, alowF1does notindicatewhichinstances areinvolved. AhighF1 indicates that we are likely to have good accuracy and memory for a significant chunk of the choice(which is informative). It's unclear what the issue is with low F1 (poor accuracy or low precision). Is Formula One merely a gimmick? No, it's frequently used and regarded a good metric for arriving at a choice, but onlywith afew changes. When youcombineFPR(falsepositiverates)with F1,youcanreducetype-I mistakes and figure outwho's to blamefor yourpoorF1 score.

24. AU-ROC(AREAUNDERRECEIVEROPERATINGCHARACTERISTICSCURVE) AUC-ROCscore/curvesarealsoknownasAUC-ROCscore/curves.Truepositive rates(TPR) andfalse positive rates(FPR) are used. TPR/recall, on the surface, is the percentage of positive data points that are correctlyclassifiedaspositivewhencomparedtoallpositivedatapoints.Toputit anotherway,thehighertheTPR,thefewerpositivedataitemswe'lloverlook. Withregardtoallnegativedatapoints,FPR/falloutreferstothefractionof NegativeDataPointsthatarewronglydeemedpositive.Toputitanotherway,the greaterthe FPR,the morenegative data pointswe'll miss.

25. We first compute the two former measures using many different thresholds for the logistic regression, and then plot them on a single graph to merge the FPR and the TPR into a single metric. The ROC curve represents the result, and the measure we use is the area underthe curve, whichwe refer to asAUROC. A no-skill classifier is one that cannot distinguish between classes and will always predict a random or constant class. The proportion of positive to negative classes affects the no- skillline.It'sahorizontallinewiththeratioofpositivecasesinthedatasetasitsvalue.It's 0.5forawell-balanceddataset. The area represents the likelihood that a randomly chosen positive example ranks higher than a randomly chosen negative example (i.e., has a higher probability of being positive thannegative).

26. As a result, a high ROC merely implies that the likelihood of a positive example being pickedat random is trulypositive. High ROC also indicates that your algorithm is good at rating test data, with the majority ofnegativeinstancesononeendofascaleandthemajorityofpositivecasesontheother. When your problem has a large class imbalance, ROC curves aren't a smart choice. The explanation for this is not obvious, but it can be deduced from the formulae; you can learn moreabout it here. After processing an imbalance set or utilizing focus loss techniques, you can still utilise them in that circumstance. Other than academic study and comparing different classifiers, theAUROC measure is useless.

27. Ihopeyounowseethevalueofperformance measures in model evaluation and are aware of a few odd Small Techniques For Decipheringyour model. One thing to keep in mind is that these metrics may be tweaked to fit your unique use case. Take, for instance,a weightedF1-score. It calculates each label's metrics and determines their average weight based on support (the number oftrue instancesfor each label) CONCLUSION

28. A weighted accuracy, or Balanced Accuracy in technical words, is another example. Tocopewithunbalanceddatasets,balancedaccuracyinbinaryandmulticlass classificationproblems is employed. It'sdefinedastheaveragerecallineachcategory.

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