It is difficult to find a good machine learning algorithm for your problem. But once you do, how do you get the best performance out of it.
In this post you will discover three ways that you can tune the parameters of a machine learning algorithm in R.
Walk through a real example step-by-step with working code in R. Use the code as a template to tune machine learning algorithms on your current or next machine learning project.
Tune Random Forest in R.
Photo bySusanne Nilsson, some rights reserved.
Get Better Accuracy From Top Algorithms
It is difficult to find a good or even a well-performing machine learning algorithm for your dataset.
Through a process of trial and error you can settle on a short list of algorithms that show promise, but how do you know which is the best.
You could use the default parameters for each algorithm. These are the parameters set by rules of thumb or suggestions in books and research papers. But how do you know the algorithms that you are settling on are showing their best performance?
Use Algorithm Tuning To Search For Algorithm Parameters
The answer is to search for good or even best combinations of algorithm parameters for your problem.
You need a process to tune each machine learning algorithm to know that you are getting the most out of it. Once tuned, you can make an objective comparison between the algorithms on your shortlist.
Searching for algorithm parameters can be difficult, there are many options, such as:
What parameters to tune?
What search method to use to locate good algorithm parameters?
What test options to use to limit overfitting the training data?
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Tune Machine Learning Algorithms in R
You can tune your machine learning algorithm parameters in R.
Generally, the approaches in this section assume that you already have a short list of well-performing machine learning algorithms for your problem from which you are looking to get better performance.
An excellent way to create your shortlist of well-performing algorithms is to use the caret package.
For more on how to use the caret package, see:
Caret R Package for Applied Predictive Modeling
In this section we will look at three methods that you can use in R to tune algorithm parameters:
Using the caret R package.
Using tools that come with the algorithm.
Designing your own parameter search.
Before we start tuning, let’s setup our environment and test data.
Test Setup
Let’s take a quick look at the data and the algorithm we will use in this case study.
Test Dataset
In this case study, we will use the sonar test problem.
This is a dataset from theUCI Machine Learning Repositorythat describes radar returns as either bouncing off metal or rocks.
It is a binary classification problem with 60 numerical input features that describe the properties of the radar return. You can learn more about this problem here:Sonar Dataset. You can see world class published results for this dataset here:Accuracy on the Sonar Dataset.
This is not a particularly difficult dataset, but is non-trivial and interesting for this example.
Let’s load the required libraries and load the dataset from themlbenchpackage.
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9library(randomForest)
library(mlbench)
library(caret)
# Load Dataset
data(Sonar)
dataset<-Sonar
x<-dataset[,1:60]
y<-dataset[,61]
Test Algorithm
We will use the popular Random Forest algorithm as the subject of our algorithm tuning.
Random Forest is not necessarily the best algorithm for this dataset, but it is a very popular algorithm and no doubt you will find tuning it a useful exercise in you own machine learning work.
When tuning an algorithm, it is important to have a good understanding of your algorithm so that you know what affect the parameters have on the model you are creating.
In this case study, we will stick to tuning two parameters, namely themtryand thentreeparameters that have the following affect on our random forest model. There are many other parameters, but these two parameters are perhaps the most likely to have the biggest effect on your final accuracy.
Direct from the help page for therandomForest()function in R:
mtry: Number of variables randomly sampled as candidates at each split.
ntree: Number of trees to grow.
Let’s create a baseline for comparison by using the recommend defaults for each parameter andmtry=floor(sqrt(ncol(x)))or mtry=7 and ntree=500.
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9# Create model with default paramters
control<-trainControl(method="repeatedcv",number=10,repeats=3)
seed<-7
metric<-"Accuracy"
set.seed(seed)
mtry<-sqrt(ncol(x))
tunegrid<-expand.grid(.mtry=mtry)
rf_default<-train(Class~.,data=dataset,method="rf",metric=metric,tuneGrid=tunegrid,trControl=control)
print(rf_default)
We can see our estimated accuracy is 81.3%.
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4Resampling results
Accuracy Kappa Accuracy SD Kappa SD
0.8138384 0.6209924 0.0747572 0.1569159
1. Tune Using Caret
The caret package in R provides an excellent facility to tune machine learning algorithm parameters.
Not all machine learning algorithms are available in caret for tuning. The choice of parameters is left to the developers of the package, namely Max Khun. Only those algorithm parameters that have a large effect (e.g. really require tuning in Khun’s opinion) are available for tuning in caret.
As such, onlymtryparameter is available in caret for tuning. The reason is its effect on the final accuracy and that it must be found empirically for a dataset.
Thentreeparameter is different in that it can be as large as you like, and continues to increases the accuracy up to some point. It is less difficult or critical to tune and could be limited more by compute time available more than anything.
Random Search
One search strategy that we can use is to try random values within a range.
This can be good if we are unsure of what the value might be and we want to overcome any biases we may have for setting the parameter (like the suggested equation above).
Let’s try a random search formtryusing caret:
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7# Random Search
control<-trainControl(method="repeatedcv",number=10,repeats=3,search="random")
set.seed(seed)
mtry<-sqrt(ncol(x))
rf_random<-train(Class~.,data=dataset,method="rf",metric=metric,tuneLength=15,trControl=control)
print(rf_random)
plot(rf_random)
Note, that we are using a test harness similar to that which we would use to spot check algorithms. Both 10-fold cross-validation and 3 repeats slows down the search process, but is intended to limit and reduce overfitting on the training set. It won’t remove overfitting entirely. Holding back a validation set for final checking is a great idea if you can spare the data.
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14Resampling results across tuning parameters:
mtry Accuracy Kappa Accuracy SD Kappa SD
11 0.8218470 0.6365181 0.09124610 0.1906693
14 0.8140620 0.6215867 0.08475785 0.1750848
17 0.8030231 0.5990734 0.09595988 0.1986971
24 0.8042929 0.6002362 0.09847815 0.2053314
30 0.7933333 0.5798250 0.09110171 0.1879681
34 0.8015873 0.5970248 0.07931664 0.1621170
45 0.7932612 0.5796828 0.09195386 0.1887363
47 0.7903896 0.5738230 0.10325010 0.2123314
49 0.7867532 0.5673879 0.09256912 0.1899197
50 0.7775397 0.5483207 0.10118502 0.2063198
60 0.7790476 0.5513705 0.09810647 0.2005012
We can see that the most accurate value for mtry was 11 with an accuracy of 82.1%.
Tune Random Forest Parameters in R Using Random Search
Grid Search
Another search is to define a grid of algorithm parameters to try.
Each axis of the grid is an algorithm parameter, and points in the grid are specific combinations of parameters. Because we are only tuning one parameter, the grid search is a linear search through a vector of candidate values.
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6control<-trainControl(method="repeatedcv",number=10,repeats=3,search="grid")
set.seed(seed)
tunegrid<-expand.grid(.mtry=c(1:15))
rf_gridsearch<-train(Class~.,data=dataset,method="rf",metric=metric,tuneGrid=tunegrid,trControl=control)
print(rf_gridsearch)
plot(rf_gridsearch)
We can see that the most accurate value for mtry was 2 with an accuracy of 83.78%.
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18Resampling results across tuning parameters:
mtry Accuracy Kappa Accuracy SD Kappa SD
1 0.8377273 0.6688712 0.07154794 0.1507990
2 0.8378932 0.6693593 0.07185686 0.1513988
3 0.8314502 0.6564856 0.08191277 0.1700197
4 0.8249567 0.6435956 0.07653933 0.1590840
5 0.8268470 0.6472114 0.06787878 0.1418983
6 0.8298701 0.6537667 0.07968069 0.1654484
7 0.8282035 0.6493708 0.07492042 0.1584772
8 0.8232828 0.6396484 0.07468091 0.1571185
9 0.8268398 0.6476575 0.07355522 0.1529670
10 0.8204906 0.6346991 0.08499469 0.1756645
11 0.8073304 0.6071477 0.09882638 0.2055589
12 0.8184488 0.6299098 0.09038264 0.1884499
13 0.8093795 0.6119327 0.08788302 0.1821910
14 0.8186797 0.6304113 0.08178957 0.1715189
15 0.8168615 0.6265481 0.10074984 0.2091663
Tune Random Forest Parameters in R Using Grid Search.png
2. Tune Using Algorithm Tools
Some algorithms provide tools for tuning the parameters of the algorithm.
For example, the random forest algorithm implementation in the randomForest package provides thetuneRF()function that searches for optimalmtryvalues given your data.
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4# Algorithm Tune (tuneRF)
set.seed(seed)
bestmtry<-tuneRF(x,y,stepFactor=1.5,improve=1e-5,ntree=500)
print(bestmtry)
You can see that the most accurate value formtrywas 10 with an OOBError of 0.1442308.
This does not really match up with what we saw in the caret repeated cross validation experiment above, wheremtry=10gave an accuracy of 82.04%. Nevertheless, it is an alternate way to tune the algorithm.
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5mtry OOBError
5.OOB 5 0.1538462
7.OOB 7 0.1538462
10.OOB 10 0.1442308
15.OOB 15 0.1682692
Tune Random Forest Parameters in R using tuneRF
3. Craft Your Own Parameter Search
Often you want to search for both the parameters that must be tuned (handled by caret) and the those that need to be scaled or adapted more generally for your dataset.
You have to craft your own parameter search.
Two popular options that I recommend are:
Tune Manually: Write R code to create lots of models and compare their accuracy using caret
Extend Caret: Create an extension to caret that adds in additional parameters to caret for the algorithm you want to tune.
Tune Manually
We want to keep using caret because it provides a direct point of comparison to our previous models (apples to apples, even the same data splits) and because of the repeated cross validation test harness that we like as it reduces the severity of overfitting.
One approach is to create many caret models for our algorithm and pass in a different parameters directly to the algorithm manually. Let’s look at an example doing this to evaluate different values forntreewhile holdingmtryconstant.
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14# Manual Search
control<-trainControl(method="repeatedcv",number=10,repeats=3,search="grid")
tunegrid<-expand.grid(.mtry=c(sqrt(ncol(x))))
modellist<-list()
for(ntreeinc(1000,1500,2000,2500)){
set.seed(seed)
fit<-train(Class~.,data=dataset,method="rf",metric=metric,tuneGrid=tunegrid,trControl=control,ntree=ntree)
key<-toString(ntree)
modellist[[key]]<-fit
}
# compare results
results<-resamples(modellist)
summary(results)
dotplot(results)
You can see that the most accuracy value forntreewas perhaps 2000 with a mean accuracy of 82.02% (a lift over our very first experiment using the default mtry value).
The results perhaps suggest an optimal value for ntree between 2000 and 2500. Also note, we held mtry constant at the default value. We could repeat the experiment with a possible bettermtry=2from the experiment above, or try combinations of of ntree and mtry in case they have interaction effects.
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9Models: 1000, 1500, 2000, 2500
Number of resamples: 30
Accuracy
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1000 0.600 0.8024 0.8500 0.8186 0.8571 0.9048 0
1500 0.600 0.8024 0.8095 0.8169 0.8571 0.9500 0
2000 0.619 0.8024 0.8095 0.8202 0.8620 0.9048 0
2500 0.619 0.8000 0.8095 0.8201 0.8893 0.9091 0
Tune Random Forest Parameters in R Manually
Extend Caret
Another approach is to create a “new” algorithm for caret to support.
This is the same random forest algorithm you are using, only modified so that it supports multiple tuning of multiple parameters.
A risk with this approach is that the caret native support for the algorithm has additional or fancy code wrapping it that subtly but importantly changes it’s behavior. You many need to repeat prior experiments with your custom algorithm support.
We can define our own algorithm to use in caret by defining a list that contains a number of custom named elements that the caret package looks for, such as how to fit and how to predict. See below for a definition of a custom random forest algorithm for use with caret that takes both an mtry and ntree parameters.
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12customRF<-list(type="Classification",library="randomForest",loop=NULL)
customRF$parameters<-data.frame(parameter=c("mtry","ntree"),class=rep("numeric",2),label=c("mtry","ntree"))
customRF$grid<-function(x,y,len=NULL,search="grid"){}
customRF$fit<-function(x,y,wts,param,lev,last,weights,classProbs,...){
randomForest(x,y,mtry=param$mtry,ntree=param$ntree,...)
}
customRF$predict<-function(modelFit,newdata,preProc=NULL,submodels=NULL)
predict(modelFit,newdata)
customRF$prob<-function(modelFit,newdata,preProc=NULL,submodels=NULL)
predict(modelFit,newdata,type="prob")
customRF$sort<-function(x)x[order(x[,1]),]
customRF$levels<-function(x)x$classes
Now, let’s make use of this custom list in our call to the caret train function, and try tuning different values for ntree and mtry.
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7# train model
control<-trainControl(method="repeatedcv",number=10,repeats=3)
tunegrid<-expand.grid(.mtry=c(1:15),.ntree=c(1000,1500,2000,2500))
set.seed(seed)
custom<-train(Class~.,data=dataset,method=customRF,metric=metric,tuneGrid=tunegrid,trControl=control)
summary(custom)
plot(custom)
This may take a minute or two to run.
You can see that the most accurate values forntreeandmtrywere 2000 and 2 with an accuracy of 84.43%.
We do perhaps see some interaction effects between the number of trees and the value ofntree. Nevertheless, if we had chosen the best value formtryfound using grid search of 2 (above) and the best value ofntreefound using grid search of 2000 (above), in this case we would have achieved the same level of tuning found in this combined search. This is a nice confirmation.
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61mtry ntree Accuracy Kappa Accuracy SD Kappa SD
1 1000 0.8442424 0.6828299 0.06505226 0.1352640
1 1500 0.8394805 0.6730868 0.05797828 0.1215990
1 2000 0.8314646 0.6564643 0.06630279 0.1381197
1 2500 0.8379654 0.6693773 0.06576468 0.1375408
2 1000 0.8313781 0.6562819 0.06909608 0.1436961
2 1500 0.8427345 0.6793793 0.07005975 0.1451269
2 2000 0.8443218 0.6830115 0.06754346 0.1403497
2 2500 0.8428066 0.6791639 0.06488132 0.1361329
3 1000 0.8350216 0.6637523 0.06530816 0.1362839
3 1500 0.8347908 0.6633405 0.06836512 0.1418106
3 2000 0.8428066 0.6800703 0.06643838 0.1382763
3 2500 0.8365296 0.6668480 0.06401429 0.1336583
4 1000 0.8316955 0.6574476 0.06292132 0.1317857
4 1500 0.8331241 0.6605244 0.07543919 0.1563171
4 2000 0.8378860 0.6699428 0.07147459 0.1488322
4 2500 0.8315368 0.6568128 0.06981259 0.1450390
5 1000 0.8284343 0.6505097 0.07278539 0.1516109
5 1500 0.8283622 0.6506604 0.07166975 0.1488037
5 2000 0.8219336 0.6375155 0.07548501 0.1564718
5 2500 0.8315440 0.6570792 0.07067743 0.1472716
6 1000 0.8203391 0.6341073 0.08076304 0.1689558
6 1500 0.8186797 0.6302188 0.07559694 0.1588256
6 2000 0.8187590 0.6310555 0.07081621 0.1468780
6 2500 0.8153463 0.6230495 0.07728249 0.1623253
7 1000 0.8217027 0.6367189 0.07649651 0.1606837
7 1500 0.8282828 0.6503808 0.06628953 0.1381925
7 2000 0.8108081 0.6147563 0.07605609 0.1573067
7 2500 0.8250361 0.6437397 0.07737756 0.1602434
8 1000 0.8187590 0.6314307 0.08378631 0.1722251
8 1500 0.8201876 0.6335679 0.07380001 0.1551340
8 2000 0.8266883 0.6472907 0.06965118 0.1450607
8 2500 0.8251082 0.6434251 0.07745300 0.1628087
9 1000 0.8121717 0.6177751 0.08218598 0.1709987
9 1500 0.8184488 0.6300547 0.08077766 0.1674261
9 2000 0.8247980 0.6429315 0.07260439 0.1513512
9 2500 0.8186003 0.6302674 0.07356916 0.1547231
10 1000 0.8235209 0.6407121 0.07991334 0.1656978
10 1500 0.8125541 0.6183581 0.06851683 0.1421993
10 2000 0.8187518 0.6308120 0.08538951 0.1782368
10 2500 0.8169336 0.6263682 0.07847066 0.1649216
11 1000 0.8203463 0.6341158 0.07222587 0.1497558
11 1500 0.8153463 0.6235878 0.09131621 0.1904418
11 2000 0.8234416 0.6402906 0.07586609 0.1576765
11 2500 0.8154906 0.6236875 0.07485835 0.1576576
12 1000 0.8201948 0.6336913 0.08672139 0.1806589
12 1500 0.8139105 0.6206994 0.08638618 0.1804780
12 2000 0.8137590 0.6204461 0.07771424 0.1629707
12 2500 0.8201876 0.6333194 0.07799832 0.1636237
13 1000 0.8123232 0.6173280 0.09299062 0.1936232
13 1500 0.8108802 0.6142721 0.08416414 0.1760527
13 2000 0.8154257 0.6236191 0.08079923 0.1693634
13 2500 0.8106566 0.6138814 0.08074394 0.1687437
14 1000 0.8171645 0.6270292 0.08608806 0.1799346
14 1500 0.8139033 0.6207263 0.08522205 0.1781396
14 2000 0.8170924 0.6276518 0.08766645 0.1822010
14 2500 0.8137590 0.6207371 0.08353328 0.1746425
15 1000 0.8091486 0.6110154 0.08455439 0.1745129
15 1500 0.8109668 0.6154780 0.08928549 0.1838700
15 2000 0.8059740 0.6047791 0.08829659 0.1837809
15 2500 0.8122511 0.6172771 0.08863418 0.1845635
Custom Tuning of Random Forest parameters in R
For more information on defining custom algorithms in caret see:
To see the actual wrapper for random forest used by caret that you can use as a starting point, see:
Random Forest Wrapper for Caret Train
Summary
In this post you discovered the importance of tuning well-performing machine learning algorithms in order to get the best performance from them.
You worked through an example of tuning the Random Forest algorithm in R and discovered three ways that you can tune a well-performing algorithm.
Using the caret R package.
Using tools that come with the algorithm.
Designing your own parameter search.
You now have a worked example and template that you can use to tune machine learning algorithms in R on your current or next project.
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