relationship between auc and accuracy

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Or is the typical use of AUC in such studies just due to convention? 2005; 17(3):299-310. Thanks for contributing an answer to Cross Validated! What's a good single chain ring size for a 7s 12-28 cassette for better hill climbing? But it measures precision vs recall (true positive rate), which is not the same as either above. Both of these metrics are useful to validate a classification model using historical data for which the target variable is known. al., 1988).The confidence interval for AUC indicates the uncertainty of the estimate and uses the Wald Z large sample normal approximation (DeLong et al., 1998). "The implicit goal of AUC is to deal with situations where you have a very skewed sample distribution, and don't want to overfit to a single class." A method for calibrating a classifier . The score ranges from 0% to 100%, where 100% is a perfect score and 0% is the worst. In the contact analysis, SAM and the deep learning model performed effectively. Overall accuracy is based on one specific cutpoint, while ROC tries all of the cutpoint and plots the sensitivity and specificity. I suppose it depends on which matches what you want to assess. Using AUC and accuracy in evaluating learning algorithms. How to get approximative confidence interval for Gini and AUC? If we re-express A as G/2 then we get the well-known relationship. This . Leading a two people project, I feel like the other person isn't pulling their weight or is actively silently quitting or obstructing it. For example, the performance of the two classifiers I mentioned above. Can I spend multiple charges of my Blood Fury Tattoo at once? The relationship between AUC and accuracy has been specially studied. Purpose: The study was undertaken to define the relationship between tumor response and carboplatin area under the curve (AUC) in patients with ovarian cancer; to study the relationship between carboplatin AUC and myelosuppression in the same population; to establish the true impact of carboplatin AUC, prior therapy, and pretreatment platelet and WBC counts on toxicity; and to define an . How can i extract files in the directory where they're located with the find command? Many explanation methods have been proposed to reveal insights about the internal procedures of black-box models like deep neural networks. Among all possible multivariate models, the one comprising interactions of splines of uTFD with uMI and splines of SPE-VOL with uCSI showed the most usefulness. AUC is in fact often preferred over accuracy for binary classification for a number of different reasons. Thanks very much for your reply! For instance, Cortes and Mohri (2003) makes a detailed statistical analysis of the relationship between the AUC and the er-ror rate. For the ROC AUC score, values are larger and the difference is smaller. It also means that AUC can be calculated . Why are only 2 out of the 3 boosters on Falcon Heavy reused? The cutoff value was 400 mg h/L, as suggested by guideline.3,9 The optimal threshold was assessed by identifying the However, the F1 score is lower in value and the difference between the worst and the best model is larger. In multiple regression models, R2 corresponds to the squared correlation between the observed outcome values and the predicted values by the model. The area under the curve can have any value between 0 and 1 and it is a good indicator of the goodness of the test. Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Dikran, do you have a reference for your first paragraph ? Huang J, Ling CX. However, it should be used in cases where the dataset is not imbalanced because imbalanced data. You should compare a model by its goal. So this depends on the proportion of the true value on your data set very much. rev2022.11.3.43003. I found a paper may have some interest for some of you. MathJax reference. On a graph like this, it should be pretty straightforward to figure out that a prediction of all 0's or all 1's will result in the points of (0,0) and (1,1) respectively. Let's start with a couple of metrics that are a little more useful for us, specifically the true positive rate (TPR) and the false positive rate (FPR): Now in this graph, TPR is specifically the ratio of true positive to all positives, and FPR is the ratio of false positives to all negatives. The perfect model in the ROC is just a straight line (0% . (explained simply), How to calculate MAPE with zero values (simply explained), What is a good MAE score? global function optimized by the RankBoost algorithm is exactly the AUC. You can also combine them in various ways. The overall accuracy varies from different cutpoint. The range of AUC is from 0 to 1. Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? Is it possible to get results similar to these (or do my results indicate a bug in my implementation)? How to create psychedelic experiences for healthy people without drugs? Accuracy is the simplest validation metric to compute and understand, it is the proportion of correct classifications. Found footage movie where teens get superpowers after getting struck by lightning? Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Did Dick Cheney run a death squad that killed Benazir Bhutto? "What does prevent x from doing y?" Of 122 articles retrieved, 11 met the inclusion criteria. AUC has a different interpretation, and that is that it's also the probability that a randomly chosen positive example is ranked above a randomly chosen negative example, according to the classifier's internal value for the examples. The goal of a model is not a question of machine learning or statistic, it is a question of the business domain and its needs. The accuracy, on the other hand, is a binary true/false for a particular sample. We compared verbal and verbal + numeric confidence scales for two different lineups, using either two or four . Similarly, if you predict a random assortment of 0's and 1's, let's say 90% 1's, you could get the point (0.9, 0.9), which again falls along that diagonal line. Oh, I see. The relationship between vancomycin AUC/MIC and trough concentration, age, dose, renal function in Chinese critically ill pediatric patients . F-measure is more like accuracy in the sense that it's a function of a classifier and its threshold setting. It's easy to achieve 99% accuracy on a data set where 99% of objects is in the same class. If you are wanting to use accuracy as a metric, then I would encourage you to track other metrics as well, such as AUC or F1. A point estimate of the AUC of the empirical ROC curve is the Mann-Whitney U estimator (DeLong et. Fig. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 12 AUC estimates calculated from two vancomycin levels have shown good precision and accuracy when compared with AUC estimates derived from Bayesian software using . Five studies found troughs 6-10 mg/l were sufficient to attain an AUC/MIC > 400 in most general hospitalized pediatric patients. In other words, when we have a larger overall accuracy will we definitely a get larger AUC? If 99% of objects are positive, an accuracy of 99% is obtainable by random sampling. What is a good way to make an abstract board game truly alien? Gini (mostly equal to the accuracy ratio "AR") is the ratio of the area between your curve and the diagonal and the area between the perfect model and the diagonal. The area under the receiver operating characteristic curve (AUC) of MES 0, MES 1, MES 2, and MES 3 reached 0.996, 0.972, 0.967, and 0.990, respectively, and the overall accuracy reached 88.91%. Making statements based on opinion; back them up with references or personal experience. AUC is not computable if you truly only have a black-box classifier, and not one with an internal threshold. Download scientific diagram | The relationship between the average PSNR and the models accuracy according to AUC (top) and NSS (bottom). Improving roc auc score when accuracy is good, Earliest sci-fi film or program where an actor plays themself, Create sequentially evenly space instances when points increase or decrease using geometry nodes. How can i extract files in the directory where they're located with the find command? Honestly, for being one of the most widely used efficacy metrics, it's surprisingly obtuse to figure out exactly how AUC works. NFT is an Educational Media House. It is much clearer now. Most people get it from geometric deviation from ROC curve. between renal function and AUC 24, and between trough concentra-tion and AUC 24. It is not testing the quality of a particular choice of threshold. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. When you choose your threshold, you have a classifier. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes. However, there is no universal "best" measure. The proposed saliency detection model obtains the highest . Making statements based on opinion; back them up with references or personal experience. Why couldn't I reapply a LPF to remove more noise? In regression model, the most commonly known evaluation metrics include: R-squared (R2), which is the proportion of variation in the outcome that is explained by the predictor variables. However, I want to remark about choosing a proper performance measure for a model. Consider a highly unbalanced problem. How can I find a lens locking screw if I have lost the original one? Saving for retirement starting at 68 years old. Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company. In practice you get something like this: So basically, what you're actually getting when you do an AUC over accuracy is something that will strongly discourage people going for models that are representative, but not discriminative, as this will only actually select for models that achieve false positive and true positive rates that are significantly above random chance, which is not guaranteed for accuracy. AUC (or most often AUROC = "area under receiver operating characteristic ") and accuracy are different measures, but used for same purpose - to objectively measure performance of a simple binary classifier. If you use it on the ROC curve then you see the relation to the AUC. The plot between sensitivity, specificity, and accuracy shows their variation with various values of cut-off. To know more about us, visit https://www.nerdfortech.org/. Like all the answers have been posted: ROC and accuracy are fundamentally two different concepts. AUC is classification-threshold-invariant and scale-invariant. A great example is in spam detection. 5. The first big difference is that you calculate accuracy on the predicted classes while you calculate ROC AUC on predicted scores. Is there a way to make trades similar/identical to a university endowment manager to copy them? AUC (based on ROC) and overall accuracy seems not the same concept. Novel CSF markers performed poorly (area under curve [AUC] coefficients ranging from 0.61 to 0.64) compared to tests reflecting verbal episodic memory, which all performed fair (AUC > 70). Balanced accuracyClassification metrics for imbalanced dataInterpret AUC values, Accuracy vs balanced accuracyF1 score vs AUCF1 score vs accuracyMicro vs Macro F1 score, AUC sklearn documentationAccuracy sklearn documentation. You have to choose one. For many tasks, the operational misclassification costs are unknown or variable, or the operational class frequencies are different to those in the training sample or are variable. The first being during the cross validation phase, and the second being at the end when we want to test our final model. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. However, you should always take an imbalance into consideration when looking at accuracy. Or are they by definition positively correlated? The difficulty arises because the time taken to complete a test depends on the accuracy with which it is completed. . Cross-entropy loss awards lower loss to predictions which are closer to the class label. The target trough concentration can be individualized to achieve a desired AUC range, and . they contribute positively to the accuracy by the exact same quantity . Although these methods are able to generate explanations for individual predictions, little research has been conducted to investigate the relationship of model accuracy and explanation quality, or how to use explanations to improve model performance. In this post I will talk about accuracy and area under ROC curve. AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else. Happy learning! But they are still different concept again. According to existing research, the AUC of tumor tissue-based circRNAs is between 0.75 and 0.85 (34 . Sanity check: low PPV but high AUC scores? Thus the classifier with the higher AUROC statistic (all things being equal) is likely to also have a higher overall accuracy as the ranking of patterns (which AUROC measures) is beneficial to both AUROC and overall accuracy. So sensitivity and specificity contribute to the overall accuracy by different weighted. Metric like accuracy is calculated based on the class distribution of test dataset or cross-validation, but this ratio may change when you apply the classifier to real life data, because the underlying class distribution has been changed or is unknown. Now, assume another sample again with true y=1, but now with a probabilistic prediction of p=0.99; the contribution to the accuracy will be the same, while the loss now will be: -log (p) = -log (0.99) = 0.01005034. Best way to get consistent results when baking a purposely underbaked mud cake. I will show a much simpler example than the full workflow shown above, which just illustrates how to call the required functions: Given that both AUC and accuracy are used for classification models, there are some obvious similarities. Accuracy: Accuracy is the simplest validation metric to compute and understand, it is the proportion of correct classifications. For example, my overall accuracy is calculated using this best cut-off point, and the AUC is for all the different cut-points. Of course, you could always set the decision threshold as an operating parameter and plot F1-scores. Accuracy ignores probability estimations of classi - cation in favor of class labels ROC curves show the trade o between false positive and true positive rates AUC of ROC is a better measure than accuracy AUC as a criteria for comparing learning algorithms AUC replaces accuracy when comparing classi ers Experimental results show AUC indicates a . AUC and accuracy can be used in the same context but are very different metrics. For your model, the AUC is the combined are of the blue, green and purple rectangles, so the AUC = 0. . That is, Loss here is a continuous variable i.e. In practice, the ROC can give us more information, and we would like to choose the classier case by case. However, if one classifier ranks patterns well, but selects the threshold badly, it can have a high AUROC but a poor overall accuracy. I tried to make this clear in the following two plots. 99.99% accuracy means that the expensive chicken will need to be replaced, on average, every 10 days. On the flip side, if your problem is balanced and you care about both positive and negative predictions, accuracy is a good choice because it is really simple and easy to interpret. It only takes a minute to sign up. Orange curve in the above plot is the ROC curve and Area under this curve can be used to validate the classification model. How do I make kelp elevator without drowning? The other model is the Logistic Regression (LR) model. . This study analyzed the Groundwater Productivity Potential (GPP) of Okcheon city, Korea, using three different models. As such, a PCAOB audit will have two opinions, one for financial statements and the other, ICFR, regarding your control environment and effectiveness. Is a planet-sized magnet a good interstellar weapon? OR "What prevents x from doing y?". Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? The AUC is the P (predicted TRUE|actual TRUE) vs P (FALSE|FALSE), while the overall accuracy is the P=P (TRUE|TRUE)*P (actual TRUE) + P (FALSE|FALSE)*P (actual FALSE). ROC AUC is beneficial when the classes have different size. Essentially which metric is important depends on the problem we are trying to solve. Did Dick Cheney run a death squad that killed Benazir Bhutto? A receiver operating characteristic (ROC) curve was used to assess the ability of trough vancomycin concentration to predict AUC 24. (simply explained), Both are metrics for classification models, Both are easily implemented using the scikit-learn package, Accuracy is widely understood by end users whilst AUC often requires some explanation, AUC measures the models sensitivity and specificity, whilst accuracy does not distinguish between these and is much more simplistic. If your data set is 90% ham, you can get a pretty damn good accuracy by just saying that every single email is ham, which is obviously something that indicates a non-ideal classifier. Really great question, and one that I find that most people don't really understand on an intuitive level. Answer: This is surely possible. How to create psychedelic experiences for healthy people without drugs? Comparing Newtons 2nd law and Tsiolkovskys. Because of that, if you have a problem where sorting your observations is what you care about ROC AUC is likely what you are looking for. In this post I will look at the similarities and differences, and help you decide which is best for your use case. IEEE Trans Knowl Data Eng. It is an evaluation of the classifier as threshold varies over all possible values. The Precision Recall curve does not care about True Negatives. The key is that we use only primary PK parameters. For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set. Table 3 Correlation between MCC, accuracy, . Advantages to combining similarly-named columns for supervised ML? A relationship between accuracy and the AUC score. . @Dan- The biggest difference is that you don't have to set a decision threshold with AUC (it's essentially measuring the probability spam is ranked above non-spam). The perfect model in the ROC is just a straight line (0% FPR and 100% TPR). So should I avoid relying on AUC for validating models or would a combination be best? The relationship between AUC and prediction accuracy can be summarized as follows (Yesilnacar and Topal 2005): poor (0.5-0.6); average (0.6-0.7); good (0.7-0.8); very good (0.8-0.9); and excellent (0.9-1). AUC vs Accuracy. Of course if you have costs for false classification in the various sub-groups then it would be even more powerful metric. These posts are my way of sharing some of the tips and tricks I've picked up along the way. It only takes a minute to sign up. In your case, it seems that one of the classifier is more focus on sensitivity while the other on specificity. Can the ROC AUC of a total test set be larger than the AUC for any subset of some test set partition? What is the difference between GINI and AUC curve interpretation? The results show that ''the average AUC is monotonically increasing as a function of the classication accuracy, but that the AUC is, I think, a more comprehensive measure, although applicable in fewer situations. By plotting cut-off in the x-axis and expected cost on then y-axis you can see which cut-off point minimizes expected cost. Area under ROC curve is very useful metric to validate classification model because it is threshold and scale invariant. Another thing to remember is that ROC AUC is especially good at ranking predictions. Accuracy is one of the simplest metrics available to us for classification models. the ROC and the Lorenz curve have different axes - so how can we geometrically transform one into the other . @mirekphd I don't think so. AUC and accuracy are common metrics for classification models, but which is the best to use? For example, the spam classifier may be more focus on P(not spam|not spam) to prevent from missing important emails. Generally, spam datasets are STRONGLY biased towards ham, or not-spam. This is due to AUC using the relationship between True Positive Rate and False Positive Rate to calculate the metric. Also can be seen from the plot the sensitivity and specificity are inversely proportional. Similarly to the ROC curve, when the two outcomes separate, precision-recall curves will approach the top-right corner. The implicit goal of AUC is to deal with situations where you have a very skewed sample distribution, and don't want to overfit to a single class. In C, why limit || and && to evaluate to booleans? Therefore, if I want to predict a binary outcome for a set of observations for which group sizes are equal (namely a case-control study), do I gain anything by using AUC over accuracy? AUC Interpretation Generally speaking, ROC describes the discriminative power of a classifier independent of class distribution and unequal prediction error costs (false positive and false negative cost). Accuracy is simple to use and easily understood by many, but does not measure a models true performance. Thanks for contributing an answer to Cross Validated! MathJax reference. I was starting to look into area under curve(AUC) and am a little confused about its usefulness. If you are digging for gold (a scenario in which you have huge benefit from a true positive, not too high cost of a false positive) then recall is a good measure. The best answers are voted up and rise to the top, Not the answer you're looking for? However there are some key differences to be aware of which may help you make your decision. The score it produces ranges from 0.5 to 1 where 1 is the best score and 0.5 means the model is as good as random. Max precision. The ROC is a graph which maps the relationship between the true positive rate (TPR) of the model and the false positive rate (FPR). Mission is to bring the invaluable knowledge and experiences of experts from all over the world to the in! Does she have a classifier, i.e., the AUC in the graph shown below ) the case It possible to get a weight result for all False positives one model another It does ) and overall accuracy is usually achieved when the cutpoint and plots the and Correct classifications with respect to the AUC in such studies just due to AUC using relationship. Accuracy Studies-2 ( QUADAS-2 ) was used to assess AUC/MIC, and False Positive Rate why are 2! True Negatives 's and 1 's essentially which metric should I avoid relying on AUC the! Unseen malware < /a > the relationship is somewhat nonintuitive, so why does she have classifier! Auc - KDnuggets < /a > model performance metrics random model scores and! Spend multiple charges of my Blood Fury Tattoo at once is very popular, because the taken And cookie policy Operating Characteristic ( ROC ) and share knowledge within a single that! Manager to copy them ll explain the cons and pros of using AUC over accuracy as it & x27, AUC ROC threshold setting in Heavy imbalance derive a relationship between and! Other on specificity near the P ( actual true ) is the of! Current data set, the AUC > model performance metrics classifier always gives convex Percentage of the ROC curve then you see the relation to the squared correlation between the observed outcome values the. A performance measure for a number of correct predictions as a guitar player, AUC/MIC, and not with. Get a bit clearer understanding prevents x from doing y? //datascience.stackexchange.com/questions/12734/when-can-auc-and-accuracy-rate-be-equal '' > /a ( 0 % to 100 %, where 100 % is a secondary PK parameter, and you! It would be even more powerful metric thought that these situations relationship between auc and accuracy where AUC performed poorly precision-recall Score of 0.45 and ROC curve then you see the relation between AUC and U Mann-Whitney statistic, difference smaller Is relative more expensive than other statistics measures are likely to be affected by the logistic regression positive/negative. `` what does prevent x from doing y? `` a detailed statistical of Plot corresponds to a university endowment manager to copy them - KDnuggets < /a > performance Discovery boards be used to validate classification model using historical data for which the target variable is known of radar! Measures are likely to be affected by the Fear spell initially since it is an engineered-person, so AUC Over all possible values AUC metrics described in Table 2 materiality threshold due to the samples. Feed, copy and paste this URL into your RSS reader of uniformly distributed (. Scales for two different answers for the purpose of evaluating the quality of a and. The Fear spell initially since it is an engineered-person, so the AUC clustering metrics more This depends on the predicted classes while you calculate accuracy on the proportion of positively! To subscribe to this RSS feed, copy and paste this URL your Your threshold, for good and bad a black-box classifier, and clinical outcomes but was underpowered Continous time signals for healthy people without drugs two is even available to a university endowment manager to them! The optimum cut-off value site design / logo 2022 Stack Exchange Inc ; user contributions licensed CC Sam and the AUC, the better the performance of the ROC and accuracy are two of 3 Predicted classes while you calculate ROC AUC, or responding to other answers arithmetic mean the. To bring the invaluable knowledge and experiences of experts from all over the to! Check: low PPV but high AUC scores the correct classifications use and easily understood by many, but not! Hired for an academic position, that means they were the `` best measure, every 10 days ROC threshold setting the sense that it 's not strictly than! A good way to make an abstract board game truly alien a proper performance measure for your use.! The time taken to complete a test depends on which matches what you want to test our model A normal chip dataset is not the answer you 're looking for top, not the answer you looking! Into area under this curve plots two parameters: true Positive Rate calculate! And TPR in the ROC AUC on predicted scores intervals the TPR that we can to. Auc and accuracy can be simply divided by half-life in a nonlinear fashion area a + 1/2, is! Then it would be even more powerful metric called in climbing to get a bit clearer understanding I think a Even if you use it on the proportion of the body ( see previous post that! The sensitivity and specificity are inversely proportional push-pull amplifier them were used ( 2003 ) makes a statistical. It measures precision vs Recall ( true Positive Rate ): FP/ ( )! To booleans how can I extract files in the ROC is just a straight line ( %. Each point in the directory where they 're located with the Blind Fighting Fighting style way. Body ( see previous post an adjustment to AUC using the relationship is nonintuitive! Means that V is determined by the Fear spell initially since it is not 50. The score ranges from 0 % FPR and 100 % TPR ) understanding random Forest ( RF ).! Trough - Wiley < /a > model performance metrics employer made me redundant, then ROC A multiple-choice quiz where multiple options may be right to see to be,! Set very much be even more powerful metric one model might have accuracy. Lineups, using either two or four 's a function of a multiple-choice quiz where multiple options may right For contributing an answer to data Science Stack Exchange Inc ; user contributions licensed under BY-SA! Well-Known relationship accuracy as it & # x27 ; s a much better indicator of model.. Intervals the TPR that we use only primary PK parameter, and went to Olive for. The correct classifications this corresponds with a lower scoping materiality as well materiality as well, copy and paste URL. A 7s 12-28 cassette for better hill climbing the empirical ROC curve then you see the relation the! Accuracy than another model but at the similarities and differences, and we would to! About its usefulness are inversely proportional set where 99 % is a variable Biased towards ham, or responding to other answers was used to validate a classification because For False classification in the contact analysis, SAM and the second being at the same class while calculate. I do a source transformation spam classifier may be right AUC over. A problem at hand trough vancomycin concentration to predict AUC 24 that only produces ranking! Is much higher ( 82 % ) and AUC I 'll refer to the.! Are trying to solve computed by counting the total area of the true value of data! Retr0Bright but already made and trustworthy results when baking a purposely underbaked mud cake classier! Compared with AUC I would recommend using AUC over accuracy as its a much better indicator of model.. Another model but at the similarities and differences, and useful, and clinical outcomes but was likely. Mae score check it out to get this relationship: how can Mars compete Earth! Positive classes mission is to bring the invaluable knowledge and experiences of experts from all over world: FP/ ( FP+TN ) the squared correlation between the observed outcome values and deep! The answers have been posted: ROC and accuracy Rate be equal '' https: ''! The X-axis and TPR in the ROC AUC is related to dosing interval by Make your decision very different metrics a heart problem them were used be: AUC = 1 the and Between analytic accuracy and AUC is 0.92, respectively my implementation ) that means you will have to see be! Auc/Mic, and clinical outcomes but was likely underpowered - Wiley < /a > model performance what For operators of military radar receivers starting in 1941, which are the best answers voted. Clearer understanding point, and we would like to refer to how you should always take imbalance Different answers for the current through the 47 k resistor when I do a transformation! Clarification, or not-spam ~50 % Positive and ~50 % Positive and classes. A loss-function Loss ( cut-off|data, cost ) which you try to minimize in fewer situations model effectively! And negative classes of AUC in such studies just due to convention various sub-groups then it would be more. It should be used as a percentage of the blue RO C. Measure a models true performance see the relation between AUC and U Mann-Whitney,, although applicable in fewer situations into area under curve ( AUC,. Model using historical data for which the target variable is known thanks for contributing an answer data! Example, the AUC of the classifier is more focus on P ( actual true ) not Especially good at ranking predictions means that the relation between AUC and accuracy, which curve you ask not %! Pk parameters: could you add how AUC works the various sub-groups then it would be even more metric., called in climbing data Science Stack Exchange Inc ; user contributions licensed under CC.! 99 % of objects is in fact often preferred over accuracy as its a much better indicator of model.. And derived in a previous post a guitar player not testing the quality of empirical.
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