Scientific publication
T. M. Lange, M. Gültas, A. O. Schmitt & F. Heinrich (2025). optRF: Optimising random forest stability by figuring out the optimum variety of timber. BMC bioinformatics, 26(1), 95.
Comply with this LINK to the unique publication.
Forest — A Highly effective Instrument for Anybody Working With Information
What’s Random Forest?
Have you ever ever wished you possibly can make higher choices utilizing information — like predicting the danger of illnesses, crop yields, or recognizing patterns in buyer habits? That’s the place machine studying is available in and one of the vital accessible and highly effective instruments on this discipline is one thing referred to as Random Forest.
So why is random forest so well-liked? For one, it’s extremely versatile. It really works nicely with many forms of information whether or not numbers, classes, or each. It’s additionally broadly utilized in many fields — from predicting affected person outcomes in healthcare to detecting fraud in finance, from bettering buying experiences on-line to optimising agricultural practices.
Regardless of the identify, random forest has nothing to do with timber in a forest — however it does use one thing referred to as Resolution Timber to make good predictions. You possibly can consider a call tree as a flowchart that guides a collection of sure/no questions primarily based on the information you give it. A random forest creates an entire bunch of those timber (therefore the “forest”), every barely totally different, after which combines their outcomes to make one closing choice. It’s a bit like asking a bunch of consultants for his or her opinion after which going with the bulk vote.
However till not too long ago, one query was unanswered: What number of choice timber do I really want? If every choice tree can result in totally different outcomes, averaging many timber would result in higher and extra dependable outcomes. However what number of are sufficient? Fortunately, the optRF bundle solutions this query!
So let’s take a look at learn how to optimise Random Forest for predictions and variable choice!
Making Predictions with Random Forests
To optimise and to make use of random forest for making predictions, we are able to use the open-source statistics programme R. As soon as we open R, we now have to put in the 2 R packages “ranger” which permits to make use of random forests in R and “optRF” to optimise random forests. Each packages are open-source and obtainable by way of the official R repository CRAN. In an effort to set up and cargo these packages, the next traces of R code could be run:
> set up.packages(“ranger”)
> set up.packages(“optRF”)
> library(ranger)
> library(optRF)Now that the packages are put in and loaded into the library, we are able to use the capabilities that these packages comprise. Moreover, we are able to additionally use the information set included within the optRF bundle which is free to make use of underneath the GPL license (simply because the optRF bundle itself). This information set referred to as SNPdata incorporates within the first column the yield of 250 wheat crops in addition to 5000 genomic markers (so referred to as single nucleotide polymorphisms or SNPs) that may comprise both the worth 0 or 2.
> SNPdata[1:5,1:5]
Yield SNP_0001 SNP_0002 SNP_0003 SNP_0004
ID_001 670.7588 0 0 0 0
ID_002 542.5611 0 2 0 0
ID_003 591.6631 2 2 0 2
ID_004 476.3727 0 0 0 0
ID_005 635.9814 2 2 0 2This information set is an instance for genomic information and can be utilized for genomic prediction which is an important instrument for breeding high-yielding crops and, thus, to struggle world starvation. The concept is to foretell the yield of crops utilizing genomic markers. And precisely for this goal, random forest can be utilized! That implies that a random forest mannequin is used to explain the connection between the yield and the genomic markers. Afterwards, we are able to predict the yield of wheat crops the place we solely have genomic markers.
Subsequently, let’s think about that we now have 200 wheat crops the place we all know the yield and the genomic markers. That is the so-called coaching information set. Let’s additional assume that we now have 50 wheat crops the place we all know the genomic markers however not their yield. That is the so-called check information set. Thus, we separate the information body SNPdata in order that the primary 200 rows are saved as coaching and the final 50 rows with out their yield are saved as check information:
> Coaching = SNPdata[1:200,]
> Take a look at = SNPdata[201:250,-1]With these information units, we are able to now take a look at learn how to make predictions utilizing random forests!
First, we obtained to calculate the optimum variety of timber for random forest. Since we need to make predictions, we use the perform opt_prediction from the optRF bundle. Into this perform we now have to insert the response from the coaching information set (on this case the yield), the predictors from the coaching information set (on this case the genomic markers), and the predictors from the check information set. Earlier than we run this perform, we are able to use the set.seed perform to make sure reproducibility despite the fact that this isn’t essential (we are going to see later why reproducibility is a matter right here):
> set.seed(123)
> optRF_result = opt_prediction(y = Coaching[,1],
+ X = Coaching[,-1],
+ X_Test = Take a look at)
Advisable variety of timber: 19000All the outcomes from the opt_prediction perform at the moment are saved within the object optRF_result, nevertheless, crucial data was already printed within the console: For this information set, we should always use 19,000 timber.
With this data, we are able to now use random forest to make predictions. Subsequently, we use the ranger perform to derive a random forest mannequin that describes the connection between the genomic markers and the yield within the coaching information set. Additionally right here, we now have to insert the response within the y argument and the predictors within the x argument. Moreover, we are able to set the write.forest argument to be TRUE and we are able to insert the optimum variety of timber within the num.timber argument:
> RF_model = ranger(y = Coaching[,1], x = Coaching[,-1],
+ write.forest = TRUE, num.timber = 19000)And that’s it! The thing RF_model incorporates the random forest mannequin that describes the connection between the genomic markers and the yield. With this mannequin, we are able to now predict the yield for the 50 crops within the check information set the place we now have the genomic markers however we don’t know the yield:
> predictions = predict(RF_model, information=Take a look at)$predictions
> predicted_Test = information.body(ID = row.names(Take a look at), predicted_yield = predictions)The information body predicted_Test now incorporates the IDs of the wheat crops along with their predicted yield:
> head(predicted_Test)
ID predicted_yield
ID_201 593.6063
ID_202 596.8615
ID_203 591.3695
ID_204 589.3909
ID_205 599.5155
ID_206 608.1031Variable Choice with Random Forests
A unique method to analysing such an information set can be to seek out out which variables are most essential to foretell the response. On this case, the query can be which genomic markers are most essential to foretell the yield. Additionally this may be achieved with random forests!
If we sort out such a activity, we don’t want a coaching and a check information set. We will merely use your entire information set SNPdata and see which of the variables are crucial ones. However earlier than we try this, we should always once more decide the optimum variety of timber utilizing the optRF bundle. Since we’re insterested in calculating the variable significance, we use the perform opt_importance:
> set.seed(123)
> optRF_result = opt_importance(y=SNPdata[,1],
+ X=SNPdata[,-1])
Advisable variety of timber: 40000One can see that the optimum variety of timber is now greater than it was for predictions. That is truly usually the case. Nonetheless, with this variety of timber, we are able to now use the ranger perform to calculate the significance of the variables. Subsequently, we use the ranger perform as earlier than however we alter the variety of timber within the num.timber argument to 40,000 and we set the significance argument to “permutation” (different choices are “impurity” and “impurity_corrected”).
> set.seed(123)
> RF_model = ranger(y=SNPdata[,1], x=SNPdata[,-1],
+ write.forest = TRUE, num.timber = 40000,
+ significance="permutation")
> D_VI = information.body(variable = names(SNPdata)[-1],
+ significance = RF_model$variable.significance)
> D_VI = D_VI[order(D_VI$importance, decreasing=TRUE),]The information body D_VI now incorporates all of the variables, thus, all of the genomic markers, and subsequent to it, their significance. Additionally, we now have straight ordered this information body in order that crucial markers are on the highest and the least essential markers are on the backside of this information body. Which implies that we are able to take a look at crucial variables utilizing the pinnacle perform:
> head(D_VI)
variable significance
SNP_0020 45.75302
SNP_0004 38.65594
SNP_0019 36.81254
SNP_0050 34.56292
SNP_0033 30.47347
SNP_0043 28.54312And that’s it! We’ve used random forest to make predictions and to estimate crucial variables in an information set. Moreover, we now have optimised random forest utilizing the optRF bundle!
Why Do We Want Optimisation?
Now that we’ve seen how simple it’s to make use of random forest and the way shortly it may be optimised, it’s time to take a better take a look at what’s taking place behind the scenes. Particularly, we’ll discover how random forest works and why the outcomes would possibly change from one run to a different.
To do that, we’ll use random forest to calculate the significance of every genomic marker however as a substitute of optimising the variety of timber beforehand, we’ll follow the default settings within the ranger perform. By default, ranger makes use of 500 choice timber. Let’s strive it out:
> set.seed(123)
> RF_model = ranger(y=SNPdata[,1], x=SNPdata[,-1],
+ write.forest = TRUE, significance="permutation")
> D_VI = information.body(variable = names(SNPdata)[-1],
+ significance = RF_model$variable.significance)
> D_VI = D_VI[order(D_VI$importance, decreasing=TRUE),]
> head(D_VI)
variable significance
SNP_0020 80.22909
SNP_0019 60.37387
SNP_0043 50.52367
SNP_0005 43.47999
SNP_0034 38.52494
SNP_0015 34.88654As anticipated, every part runs easily — and shortly! The truth is, this run was considerably quicker than once we beforehand used 40,000 timber. However what occurs if we run the very same code once more however this time with a distinct seed?
> set.seed(321)
> RF_model2 = ranger(y=SNPdata[,1], x=SNPdata[,-1],
+ write.forest = TRUE, significance="permutation")
> D_VI2 = information.body(variable = names(SNPdata)[-1],
+ significance = RF_model2$variable.significance)
> D_VI2 = D_VI2[order(D_VI2$importance, decreasing=TRUE),]
> head(D_VI2)
variable significance
SNP_0050 60.64051
SNP_0043 58.59175
SNP_0033 52.15701
SNP_0020 51.10561
SNP_0015 34.86162
SNP_0019 34.21317As soon as once more, every part seems to work nice however take a better take a look at the outcomes. Within the first run, SNP_0020 had the very best significance rating at 80.23, however within the second run, SNP_0050 takes the highest spot and SNP_0020 drops to the fourth place with a a lot decrease significance rating of 51.11. That’s a major shift! So what modified?
The reply lies in one thing referred to as non-determinism. Random forest, because the identify suggests, includes quite a lot of randomness: it randomly selects information samples and subsets of variables at varied factors throughout coaching. This randomness helps forestall overfitting however it additionally implies that outcomes can fluctuate barely every time you run the algorithm — even with the very same information set. That’s the place the set.seed() perform is available in. It acts like a bookmark in a shuffled deck of playing cards. By setting the identical seed, you make sure that the random decisions made by the algorithm observe the identical sequence each time you run the code. However whenever you change the seed, you’re successfully altering the random path the algorithm follows. That’s why, in our instance, crucial genomic markers got here out otherwise in every run. This habits — the place the identical course of can yield totally different outcomes attributable to inner randomness — is a traditional instance of non-determinism in machine studying.
As we simply noticed, random forest fashions can produce barely totally different outcomes each time you run them even when utilizing the identical information as a result of algorithm’s built-in randomness. So, how can we cut back this randomness and make our outcomes extra secure?
One of many easiest and only methods is to extend the variety of timber. Every tree in a random forest is skilled on a random subset of the information and variables, so the extra timber we add, the higher the mannequin can “common out” the noise attributable to particular person timber. Consider it like asking 10 individuals for his or her opinion versus asking 1,000 — you’re extra prone to get a dependable reply from the bigger group.
With extra timber, the mannequin’s predictions and variable significance rankings are likely to turn out to be extra secure and reproducible even with out setting a selected seed. In different phrases, including extra timber helps to tame the randomness. Nonetheless, there’s a catch. Extra timber additionally imply extra computation time. Coaching a random forest with 500 timber would possibly take just a few seconds however coaching one with 40,000 timber might take a number of minutes or extra, relying on the scale of your information set and your pc’s efficiency.
Nonetheless, the connection between the soundness and the computation time of random forest is non-linear. Whereas going from 500 to 1,000 timber can considerably enhance stability, going from 5,000 to 10,000 timber would possibly solely present a tiny enchancment in stability whereas doubling the computation time. Sooner or later, you hit a plateau the place including extra timber provides diminishing returns — you pay extra in computation time however acquire little or no in stability. That’s why it’s important to seek out the appropriate stability: Sufficient timber to make sure secure outcomes however not so many who your evaluation turns into unnecessarily gradual.
And that is precisely what the optRF bundle does: it analyses the connection between the soundness and the variety of timber in random forests and makes use of this relationship to find out the optimum variety of timber that results in secure outcomes and past which including extra timber would unnecessarily enhance the computation time.
Above, we now have already used the opt_importance perform and saved the outcomes as optRF_result. This object incorporates the details about the optimum variety of timber however it additionally incorporates details about the connection between the soundness and the variety of timber. Utilizing the plot_stability perform, we are able to visualise this relationship. Subsequently, we now have to insert the identify of the optRF object, which measure we’re all in favour of (right here, we have an interest within the “significance”), the interval we need to visualise on the X axis, and if the beneficial variety of timber ought to be added:
> plot_stability(optRF_result, measure="significance",
+ from=0, to=50000, add_recommendation=FALSE)
This plot clearly exhibits the non-linear relationship between stability and the variety of timber. With 500 timber, random forest solely results in a stability of round 0.2 which explains why the outcomes modified drastically when repeating random forest after setting a distinct seed. With the beneficial 40,000 timber, nevertheless, the soundness is close to 1 (which signifies an ideal stability). Including greater than 40,000 timber would get the soundness additional to 1 however this enhance can be solely very small whereas the computation time would additional enhance. That’s the reason 40,000 timber point out the optimum variety of timber for this information set.
The Takeaway: Optimise Random Forest to Get the Most of It
Random forest is a robust ally for anybody working with information — whether or not you’re a researcher, analyst, scholar, or information scientist. It’s simple to make use of, remarkably versatile, and extremely efficient throughout a variety of purposes. However like every instrument, utilizing it nicely means understanding what’s taking place underneath the hood. On this publish, we’ve uncovered one in all its hidden quirks: The randomness that makes it robust may also make it unstable if not fastidiously managed. Thankfully, with the optRF bundle, we are able to strike the right stability between stability and efficiency, guaranteeing we get dependable outcomes with out losing computational sources. Whether or not you’re working in genomics, medication, economics, agriculture, or some other data-rich discipline, mastering this stability will provide help to make smarter, extra assured choices primarily based in your information.