Tag Archives: model evaluation

Machine Learning quiz – part 3 of 3

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In my last two posts I published part 1 and part 2 of this Machine Learning quiz. If you have not read them, please do (and cast your votes) before you read part 3 below.

QUIZ, part 3: vote responses and (some) answers

In part 1 I asked which predictions looked “better”: those from model A or those from model B (Figure 1)?

Figure 1

As a reminder, both model A and model B were trained to predict the same labeled facies picked by a geologist on core, shown on the left columns (they are identical) of the respective model panels. The right columns in each panels are the predictions.

The question is asked out of context, with no information given about the training process, and or difference in data manipulation (if any) and/or model algorithm used. Very unfair, I know!  And yet, ~78% of 54 respondent clearly indicated their preference for model A. My sense is that this is because model A looks overall smoother and has less of the extra misclassified thin layers.

Response 1

In part 2, I presented the two predictions, this time accompanied by a the confusion matrix for each model (Figure 2).

Figure 2

I asked again which model would be considered better [1] and this was the result:

Response 2a

Although there were far fewer votes (not as robust a statistical sample) I see that the proportion of votes is very similar to that in the previous response, and decidedly in favor of model A, again. However, the really interesting learning, and to me surprising, came from the next answer (Response 2b): about 82% of the 11 respondents believe the performance scores in the confusion matrix to be realistic.

Response 2b

Why was it a surprise? It is now time to reveal the trick…..

…which is that the scores in part 2, shown in the confusion matrices of Figure 2, were calculated on the whole well, for training and testing together!!

A few more details:

  • I used default parameters for both models
  • I used a single 70/30 train/test split (the same random split for both models) with no crossvalidation

which is, in essence, how to NOT do Machine Learning!

In Figure 3, I added a new column on the right of each prediction showing in red which part of the result is merely memorized, and in black which part is interpreted (noise?). Notice that for this particular well (the random 70/30 split was done on all wells together) the percentages are 72.5% and 27.5%.

I’ve also added the proper confusion matrix for each model, which used only the test set. These are more realistic (and poor) results.

Figure 3

So, going back to that last response: again, with 11 votes I don’t have solid statistics, but with that caveat in mind one might argue that this is a way you could be ‘sold’ unrealistic (as in over-optimistic) ML results.

At least you could sell them by being vague about the details to those not familiar with the task of machine classification of rock facies and its difficulties (see for example this paper for a great discussion about resolution limitations inherent  in using logs (machine) as opposed to core (human geologist).

Acknowledgments

A big thank you goes to Jesper (Way of the Geophysicist) for his encouragement and feedback, and for brainstorming with me on how to deliver this post series.

[1] notice that, as pointed out in part 2, model predictions were slightly different from those part 1 because I’d forgotten to set the random seed to be the same in the two pipelines; but not very much, the overall ‘look’ was very much the same.

Machine Learning quiz – part 2 of 3

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In my previous post I posted part 1 (of 3) of a Machine Learning quiz. If you have not read that post, please do, cast your vote, then come back and try part 2 below.

QUIZ, part 2

Just as a quick reminder, the image below shows the rock facies predicted from two models, which I just called A and B. Both were trained to predict the same labeled rock facies, picked by a geologist on core, which are shown on the left columns (they are identical) of the respective model panels. The right columns in each panels are the predictions.

*** Please notice that the models in this figure are (very slightly) different from part 1 because I’d forgotten to set the random seed to be the same in the two pipelines (yes, it happens, my apology). But they are not so different, so I left the image in part 1 unchanged and just updated this one.

Please answer the first question: which model predicts the labeled facies “better” (visually)?

Now study the performance as summarized in the confusion matrices for each model (the purple arrows indicate to which model each matrix belongs; I’ve highlighted in green the columns where each model does better, based on F1 (you don’t have to agree with my choice), and answer the second question (notice the differences are often a few 1/100s, or just one).

 

Machine Learning quiz – part 1 of 3

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Introduction

I’ve been meaning to write about the 2016 SEG Machine Learning Contest for some time. I am thinking of a short and not very structured series (i.e. I’ll jump all over the place) of 2, possibly 3 posts (with the exclusion of this quiz). It will mostly be a revisiting – and extension – of some work that team MandMs (Mark Dahl and I) did, but not necessarily posted. I will touch most certainly on cross-validation, learning curves, data imputation, maybe a few other topics.

Background on the 2016 ML contest

The goal of the SEG contest was for teams to train a machine learning algorithm to predict rock facies from well log data. Below is the (slightly modified) description of the data form the original notebook by Brendon Hall:

The data is originally from a class exercise from The University of Kansas on Neural Networks and Fuzzy Systems. This exercise is based on a consortium project to use machine learning techniques to create a reservoir model of the largest gas fields in North America, the Hugoton and Panoma Fields. For more info on the origin of the data, see Bohling and Dubois (2003) and Dubois et al. (2007).

This dataset is from nine wells (with 4149 examples), consisting of a set of seven predictor variables and a rock facies (class) for each example vector and validation (test) data (830 examples from two wells) having the same seven predictor variables in the feature vector. Facies are based on examination of cores from nine wells taken vertically at half-foot intervals. Predictor variables include five from wireline log measurements and two geologic constraining variables that are derived from geologic knowledge. These are essentially continuous variables sampled at a half-foot sample rate.

The seven predictor variables are:

The nine discrete facies (classes of rocks) are:

For some examples of the work during the contest, you can take a look at the original notebook, one of the submissions by my team, where we used Support Vector Classification to predict the facies, or a submission by the one of the top 4 teams, all of whom achieved the highest scores on the validation data with different combinations of Boosted Trees trained on augmented features alongside the original features.

QUIZ

Just before last Christmas, I run a little fun experiment to resume work with this dataset. I decided to turn the outcome into a quiz.

Below I present the predicted rock facies from two distinct models, which I call A and B. Both were trained to predict the same labeled facies picked by the geologist, which are shown on the left columns (they are identical) of the respective model panels. The right columns in each panels are the predictions. Which predictions are “better”?

Please be warned, the question is a trick one. As you can see, I am gently leading you to make a visual, qualitative assessment of “better-ness”, while being absolutely vague about the models and not giving any information about the training process, which is intentional, and – yes! – not very fair. But that’s the whole point of this quiz, which is really a teaser to the series.