My machine learning model has precision of 30%. Can this model be useful?

Question

I've encountered an interesting discussion at work on interpretation of precision (confusion matrix) within a machine learning model. The interpretation of precision is where there is a difference of opinion so my description centers a little bit simplified around precision.

The problem discussed with some numbers for reference:

Suppose we have a machine learning model A for binary classification. The training dataset has 100.000 datapoints. The data is quite imbalanced: 5% is classified as 1, 95% as 0. The model is tested on another (unseen) dataset of 50.000 datapoints. For evaluation a confusion matrix is made to evaluate model A. Precision (TP/(TP+FP) = 30%. From here on there is a difference in view between the data scientists (both camps are intelligent people).

Group 1: We think the model is useful. While precision is low (30%), it is quite higher than random (5%). Therefore the model has some value. We can use the output the model generates and can expect the model to pinpoint datapoints which on average have a 30% probability of being a 1.

Group 2: You can not use this model. Precision needs to be at minimum 70-80% for a model to be useful. The point is, precision only measures the model, and not the underlying data. Therefore one can only use models with a minimum of 70-80% precision. Balanced or imbalanced data doesn't matter.

I myself find myself more in group 1. So if someone can explain why group 2 is right (if they are right) I would be happy.

More context: We produced a model to pinpoints locations where inspectors can find objects with on error. In the past ( al the datapoints) inspectors found on average in 5% of their visits an error. Model A had a precision of 30%. So the reasoning of group 1 is if inspectors only go to these 30% locations pinpointed by the model they will on average find more errors than the historic 5%. I should ad that only about 500 visits per year will be done, false negatives have no associated costs. So any precision gain more than 5% would be good.

Answer 1

As Dave argues, if "false negatives have no associated costs", then your best course of action would be not to classify anything as positive, i.e., as 1. You inspect nothing at all, you incur zero cost, and all your inspectors can do something different (or be fired).

Yes, of course this makes no sense. Which is because it makes no sense to claim that false negatives incur no costs. They do, it's the cost of uncaught errors.

This is an example of why precision, sensitivity etc. are all as misleading as accuracy as evaluation metrics, especially (but not only) in "unbalanced" situations.

What I would strongly recommend you do is scrap "hard" classifications, use probabilistic classifications instead and separate the decision aspect from the statistical modeling aspect. The decisions (whether to inspect or not) should not only be driven by the probabilistic classification, but also by the cost structure.

Answer 2

I would say neither group is entirely correct. The question is what do you want to do with the model, and what will happen for positive or negative model predictions?

There are screening tests used in medical practice that have precision (positive predictive value) that low -- mammograms for breast cancer, prostate-specific antigen for prostate cancer. The positive predictive value of low-dose spiral CT for detecting lung cancer in smokers is way lower than 30%.

What these have in common is that you really want to detect cases, so you care much more about sensitivity (recall) than precision. The benefit of a true positive is much more than 3 times the cost of a false positive.

So, what you need to know to decide is the two costs ('losses' in decision theory). You can then work out the expected loss from using the algorithm and from not using it, and see which is lower.

Answer 3

This primarily depends on how the model is supposed to be used. From your context it seems you have an alternative test which has an almost perfect classification rate but is very expensive to use because it essentially consists of sending a qualified human to do a manual check. You want to use your model (which in comparision is extremely cheap) to decide where to perform the human tests.

If I understand you correctly than a) your goal is to find as many instances evaluated by a human as 1 as possible (implying instances evaluated as 0 are not valuable) and b) if some instances that are 1 are not checked by a human because the model thinks they are unlikely candidates, this is not a problem because the number of human checks is very limited anyway.

If these assumptions are correct than any model that has a bigger than 5% chance to find instances qualified as 1 is useful and your model with a 30% hit rate will increase the number of instances qualified as 1 sixfold so is a major improvement compared to not using a the model.

There are of course plenty of other situations where such a model would be useless or even actively bad if applied. It just depends on what you want to do with the model.

Answer 4

As with most things, neither of two polar opinions is wholly correct

If a model can select an area needed for inspection better than random chance then for a singular inspection run you're using your inspector's time more usefully.

As an example, lets say your inspectors are checking welds on a pipeline. A classification model for error/no error may pick up early signs of corrosion but not spot a more subtle error like porosity or an undercut. In the long term, dependence on the model to inform inspectors could mean other error type get inspected less.

I'd recommend trying to look into your false negatives here, I know you say they incur no cost but you've got inspectors for a reason. Is there a particular type of defect that your model isn't picking up? Is there more data you can bring in to better account for the other error types?

tldr;

Better than random sounds like it would be more effective, but if your model is blind to an error type it could be increasing the likelihood that that error goes unnoticed (without knowing the specific situation we can't say more). At the same time 70-80% is just a number picked from thin air. A lot is down to your application.

Answer 5

5% would not be the performance of the model making predictions “at random” or if you just classified everything as 1’s.

I'm not sure if comparing it to the base rate makes sense here. It means comparing to the most primitive alternative possible. Why not try some other simple model (decision tree, logistic regression, $k$NN) as a benchmark?

Moreover, there's nothing magical about “70-80% precision”. For some problems, this would not be achievable, but for others way too low. Those numbers are arbitrary and there's no reason whatsoever to aim at them.

Answer 6

As hashed over a bit in the comments. The usefulness of the model here is going to hinge heavily on how likely it is that the distribution of your future data is to match the training/validation data.

I think it would be a serious mistake to assume they will match unless you could think of a super obvious reason why they wouldn't. I would assume there will be differences and test that hypothesis. If the generation of this data is stationary and highly likely to remain similar, then the model is probably useful, but you should make an attempt to quantify this (e.g. if your current data was gathered at different times, separate it into the earliest and latest data and check model performance within each subgroup and see if there are time-based trends in the data itself (look for time/geography/population-based trends in a wide range of statistics, means, medians, maxes, mins, curl, etc...) or if there is any other potential reason the data might change, e.g. new professional guidelines). I wouldn't recommend assuming the model will generalize even with good looking generalization on train/test/validation sets until you get a new real-world independent validation set. And then the model might work just for your company with exactly how you gather data right now, if you outsource the data gathering any sort of new semi-systemic idiosyncrasy might throw a serious wrench in the works. Likewise, it may not work at another branch office, etc...

This sort of issue is a plague in precision medicine, in part because the large whole genome and exome data sets and GWAS studies are so biased towards white Europeans but when you want to go to clinical application you're suddenly not treating only white Europeans. Then there are false positives... you can have a SNP associated with their descent look associated with disease but have it turn out to be socioeconomic rather than genetic, etc... This is in part why ML hasn't obliterated much simpler statistical tests in that field. One of the rationales some have offered for including more diverse populations in GWAS is merely to reduce the false positives showing up for the currently available data. I try not to read that too cynically.

Also, I'm aware of various attempts to use more recent ML methods (deep learning, gradient boosted trees, xgboost) for imputation in this field, but none of them have broken into mainstream use despite very flattering initial papers. Largely because when they are applied to new independent data they don't perform better than the HMMs and often quite a bit worse.

When group B says they want to see much better performance, the actual threshold is arbitrary, but I think the sentiment is that they expect some loss of utility due to data differences and, unless the initial performance was strong, expect it is likely for the edge the model has to evaporate or even be harmful.

Edit: I read now the comments where you say it is a logistic regression. Usually people tend not to say "My machine learning model" when it's a logistic regression even though it can indeed be considered machine learning. You are certainly less likely to have some of the issues described above when using simpler models like that, but it can still happen. I do lean towards it being useful, but it can still be worth validating the common assumptions (e.g. stationality).

Answer 7

It depends.

A decision model can only be considered "useful" or not given a particular setting. You can see this in everyday life, where different decision processes result in a wide range of performance measures, from near-perfect to slightly-better-than-random. Take, for example, a few different decision processes, like 1) diagnosing a severe medical issue, 2) returning top search hits, and 3) selecting batches of parts for inspection. In each of these cases, one should conider the relative costs of false positives and negatives, and tune the decision process to optimize for the desired metric.

A medical screening test, for example, should have high recall, catching all true case of disease at the cost of some false diagnoses. A search engine, on the other hand, should have high precision, as it doesn't need to find every relevant webpage, but the ones it does return must be relevant. An industrial decision tool to direct parts inspectors may be useful even if it is only slightly better than random, as virtually any improvement makes the inspectors more efficient.

As you can see, there is not a numerical threshold for any performance measure that separates useful and non-useful models. It all depends on the context. A decision model with 30% precision may indeed be useful in some settings, but not others.

Answer 8

This question requires answers to two sub questions. When is a model useful? What is the cost function to determine usefulness?

Group 1 says that the model is useful because it is doing better than nothing.

Group 2 says that the model is not useful because the cost is only reduced in a meaningful way when the performance is above some level (apparently 70-80% for your colleagues).

The two group's conclusion don't really contradict, they just look at it with different perspectives based on answers to the subquestions.