mmetric: Compute classification or regression error metrics.

In rminer: Data Mining Classification and Regression Methods

Compute classification or regression error metrics.

Description

Compute classification or regression error metrics.

Usage

mmetric(y, x = NULL, metric, D = 0.5, TC = -1, val = NULL, aggregate = "no")

Arguments

y	if there are predictions (!is.null(x)), y should be a numeric vector or factor with the target desired responses (or output values). Else, y should be a list returned by the mining function.
x	the predictions (should be a numeric vector if task="reg", matrix if task="prob" or factor if task="class" (used if y is not a list).
metric	a R function or a character. Note: if a R function, then it should be set to provide lower values for better models if the intention is to be used within the search argument of fit and mining (i.e., "<" meaning). Valid character options are (">" means "better" if higher value; "<" means "better" if lower value): ALL – returns all classification or regression metrics (context dependent, multi-metric). if vector – returns all metrics included in the vector, vector elements can be any of the options below (multi-metric). CONF – confusion matrix (classification, matrix). ACC – classification accuracy rate, equal to micro averaged F1 score (classification, ">", [0-%100]). macroACC – macro average ACC score, for multiclass tasks (classification, ">", [0-%100]). weightedACC – weighted average ACC score, for multiclass tasks (classification, ">", [0-%100]). CE – classification error or misclassification error rate (classification, "<", [0-%100]). MAEO – mean absolute error for ordinal classification (classification, "<", [0-Inf[). MSEO – mean squared error for ordinal classification (classification, "<", [0-Inf[). KENDALL – Kendalls's coefficient for ordinal classification or (mean if) ranking (classification, ">", [-1;1]). Note: if ranking, y is a matrix and mean metric is computed. SPEARMAN – Mean Spearman's rho coefficient for ranking (classification, ">", [-1;1]). Note: if ranking, y is a matrix and mean metric is computed. BER – balanced error rate (classification, "<", [0-%100]). KAPPA – kappa index (classification, "<", [0-%100]). CRAMERV – Cramer's V (classification, ">", [0,1.0]). ACCLASS – classification accuracy rate per class (classification, ">", [0-%100]). BAL_ACC – balanced accuracy rate per class (classification, ">", [0-%100]). TPR – true positive rate, sensitivity or recall (classification, ">", [0-%100]). macroTPR – macro average TPR score, for multiclass tasks (classification, ">", [0-%100]). weightedTPR – weighted average TPR score, for multiclass tasks (classification, ">", [0-%100]). TNR – true negative rate or specificity (classification, ">", [0-%100]). macroTNR – macro average TNR score, for multiclass tasks (classification, ">", [0-%100]). weightedTNR – weighted average TNR score, for multiclass tasks (classification, ">", [0-%100]). microTNR – micro average TNR score, for multiclass tasks (classification, ">", [0-%100]). PRECISION – precision (classification, ">", [0-%100]). macroPRECISION – macro average precision, for multiclass tasks (classification, ">", [0-%100]). weightedPRECISION – weighted average precision, for multiclass tasks (classification, ">", [0-%100]). F1 – F1 score (classification, ">", [0-%100]). macroF1 – macro average F1 score, for multiclass tasks (classification, ">", [0-%100]). weightedF1 – weighted average F1 score, for multiclass tasks (classification, ">", [0-%100]). MCC – Matthews correlation coefficient (classification, ">", [-1,1]). BRIER – overall Brier score (classification "prob", "<", [0,1.0]). BRIERCLASS – Brier score per class (classification "prob", "<", [0,1.0]). ROC – Receiver Operating Characteristic curve (classification "prob", list with several components). AUC – overall area under the curve (of ROC curve, classification "prob", ">", domain values: [0,1.0]). AUCCLASS – area under the curve per class (of ROC curve, classification "prob", ">", domain values: [0,1.0]). NAUC – normalized AUC (given a fixed val=FPR, classification "prob", ">", [0,1.0]). TPRATFPR – the TPR (given a fixed val=FPR, classification "prob", ">", [0,1.0]). LIFT – accumulative percent of responses captured (LIFT accumulative curve, classification "prob", list with several components). ALIFT – area of the accumulative percent of responses captured (LIFT accumulative curve, classification "prob", ">", [0,1.0]). NALIFT – normalized ALIFT (given a fixed val=percentage of examples, classification "prob", ">", [0,1.0]). ALIFTATPERC – ALIFT value (given a fixed val=percentage of examples, classification "prob", ">", [0,1.0]). SAE – sum absolute error/deviation (regression, "<", [0,Inf[). MAE – mean absolute error (regression, "<", [0,Inf[). MdAE – median absolute error (regression, "<", [0,Inf[). GMAE – geometric mean absolute error (regression, "<", [0,Inf[). MaxAE – maximum absolute error (regression, "<", [0,Inf[). NMAE – normalized mean absolute error (regression, "<", [0%,Inf[). Note: by default, this metric assumes the range of y as the denominator of NMAE; a different range can be set by setting the optional val argument (see example). RAE – relative absolute error (regression, "<", [0%,Inf[). SSE – sum squared error (regression, "<", [0,Inf[). MSE – mean squared error (regression, "<", [0,Inf[). MdSE – median squared error (regression, "<", [0,Inf[). RMSE – root mean squared error (regression, "<", [0,Inf[). GMSE – geometric mean squared error (regression, "<", [0,Inf[). HRMSE – Heteroscedasticity consistent root mean squared error (regression, "<", [0,Inf[). RSE – relative squared error (regression, "<", [0%,Inf[). RRSE – root relative squared error (regression, "<", [0%,Inf[). ME – mean error (regression, "<", [0,Inf[). SMinkowski3 – sum of Minkowski loss function (q=3, heavier penalty for large errors when compared with SSE, regression, "<", [0%,Inf[). MMinkowski3 – mean of Minkowski loss function (q=3, heavier penalty for large errors when compared with SSE, regression, "<", [0%,Inf[). MdMinkowski3 – median of Minkowski loss function (q=3, heavier penalty for large errors when compared with SSE, regression, "<", [0%,Inf[). COR – Pearson correlation (regression, ">", [-1,1]). q2 – =1-correlation^2 test error metric, as used by M.J. Embrechts (regression, "<", [0,1.0]). R2 – coefficient of determination R^2 (regression, ">", squared pearson correlation coefficient: [0,1]). R22 – 2nd variant of coefficient of determination R^2 (regression, ">", most general definition that however can lead to negative values: ]-Inf,1]. In previous rminer versions, this variant was known as "R2"). EV – explained variance, 1 - var(y-x)/var(y) (regression, ">", ]-Inf,1]). Q2 – R^2/SD test error metric, as used by M.J. Embrechts (regression, "<", [0,Inf[). REC – Regression Error Characteristic curve (regression, list with several components). NAREC – normalized REC area (given a fixed val=tolerance, regression, ">", [0,1.0]). TOLERANCE – the tolerance (y-axis value) of a REC curve given a fixed val=tolerance value, regression, ">", [0,1.0]). TOLERANCEPERC – the tolerance (y-axis value) of a REC curve given a percentage val= value (in terms of y range), regression, ">", [0,1.0]). MAPE – Mean Absolute Percentage mmetric forecasting metric (regression, "<", [0%,Inf[). MdAPE – Median Absolute Percentage mmetric forecasting metric (regression, "<"), [0%,Inf[). RMSPE – Root Mean Square Percentage mmetric forecasting metric (regression, "<", [0%,Inf[). RMdSPE – Root Median Square Percentage mmetric forecasting metric (regression, "<", [0%,Inf[). SMAPE – Symmetric Mean Absolute Percentage mmetric forecasting metric (regression, "<", [0%,200%]). SMdAPE – Symmetric Median Absolute Percentage mmetric forecasting metric (regression, "<", [0%,200%]). MRAE – Mean Relative Absolute mmetric forecasting metric (val should contain the last in-sample/training data value (for random walk) or full benchmark time series related with out-of-sample values, regression, "<", [0,Inf[). MdRAE – Median Relative Absolute mmetric forecasting metric (val should contain the last in-sample/training data value (for random walk) or full benchmark time series, regression, "<", [0,Inf[). GMRAE – Geometric Mean Relative Absoluate mmetric forecasting metric (val should contain the last in-sample/training data value (for random walk) or full benchmark time series, regression, "<", [0,Inf[). THEILSU2 – Theils'U2 forecasting metric (val should contain the last in-sample/training data value (for random walk) or full benchmark time series, regression, "<", [0,Inf[). MASE – MASE forecasting metric (val should contain the time series in-samples or training data, regression, "<", [0,Inf[).
D	decision threshold (for task="prob", probabilistic classification) within [0,1]. The class is TRUE if prob>D.
TC	target class index or vector of indexes (for multi-class classification class) from 1 to Nc, where Nc is the number of classes:<cr> if TC==-1 (the default value), then it is assumed: if metric is "CONF" – D is ignored and highest probability class is assumed (if TC>0, the metric is computed for positive TC class and D is used). if metric is "ACC", "CE", "BER", "KAPPA", "CRAMERV", "BRIER", or "AUC" – the global metric (for all classes) is computed (if TC>0, the metric is computed for positive TC class). if metric is "ACCLASS", "TPR", "TNR", "Precision", "F1", "MCC", "ROC", "BRIERCLASS", "AUCCLASS" – it returns one result per class (if TC>0, it returns negative (e.g. "TPR1") and positive (TC, e.g. "TPR2") result). if metric is "NAUC", "TPRATFPR", "LIFT", "ALIFT", "NALIFT" or "ALIFTATPERC" – TC is set to the index of the last class.
val	auxiliary value: when two or more metrics need different val values, then val should be a vector list, see example. if numeric or vector – check the metric argument for specific details of each metric val meaning.
aggregate	character with type of aggregation performed when y is a mining list. Valid options are: no – returns all metrics for all mining runs. If metric includes "CONF", "ROC", "LIFT" or "REC", it returns a vector list, else if metric includes a single metric, it returns a vector; else it retuns a data.frame (runs x metrics). sum – sums all run results. mean – averages all run results. note: both "sum" and "mean" only work if only metric=="CONF" is used or if metric does not contain "ROC", "LIFT" or "REC".

Details

Compute classification or regression error metrics:

• mmetric – compute one or more classification/regression metrics given y and x OR a mining list.

• metrics – deprecated function, same as mmetric(x,y,metric="ALL"), included here just for compatability purposes but will be removed from the package.

Value

Returns the computed error metric(s):

• one value if only one metric is requested (and y is not a mining list);

• named vector if 2 or more elements are requested in metric (and y is not a mining list);

• list if there is a "CONF", "ROC", "LIFT" or "REC" request on metric (other metrics are stored in field $res, and y is not a mining list).

• if y is a mining list then there can be several runs, thus:

  • a vector list of size y$runs is returned if metric includes "CONF", "ROC", "LIFT" or "REC" and aggregate="no";

  • a data.frame is returned if aggregate="no" and metric does not include "CONF", "ROC", "LIFT" or "REC";

  • a table is returned if aggregate="sum" or "mean" and metric="CONF";

  • a vector or numeric value is returned if aggregate="sum" or "mean" and metric is not "CONF".

Note

See also http://hdl.handle.net/1822/36210 and http://www3.dsi.uminho.pt/pcortez/rminer.html

Author(s)

Paulo Cortez http://www3.dsi.uminho.pt/pcortez/

References

• To check for more details about rminer and for citation purposes:
  P. Cortez.
  Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool.
  In P. Perner (Ed.), Advances in Data Mining - Applications and Theoretical Aspects 10th Industrial Conference on Data Mining (ICDM 2010), Lecture Notes in Artificial Intelligence 6171, pp. 572-583, Berlin, Germany, July, 2010. Springer. ISBN: 978-3-642-14399-1.
  @Springer: https://link.springer.com/chapter/10.1007/978-3-642-14400-4_44
  http://www3.dsi.uminho.pt/pcortez/2010-rminer.pdf
  

• This tutorial shows additional code examples:
  P. Cortez.
  A tutorial on using the rminer R package for data mining tasks.
  Teaching Report, Department of Information Systems, ALGORITMI Research Centre, Engineering School, University of Minho, Guimaraes, Portugal, July 2015.
  http://hdl.handle.net/1822/36210
  

• About the Brier and Global AUC scores:
  A. Silva, P. Cortez, M.F. Santos, L. Gomes and J. Neves.
  Rating Organ Failure via Adverse Events using Data Mining in the Intensive Care Unit.
  In Artificial Intelligence in Medicine, Elsevier, 43 (3): 179-193, 2008.
  \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.artmed.2008.03.010")}
  

• About the classification and regression metrics:
  I. Witten and E. Frank.
  Data Mining: Practical machine learning tools and techniques.
  Morgan Kaufmann, 2005.

• About the forecasting metrics:
  R. Hyndman and A. Koehler
  Another look at measures of forecast accuracy.
  In International Journal of Forecasting, 22(4):679-688, 2006.
  

• About the ordinal classification metrics:
  J.S. Cardoso and R. Sousa.
  Measuring the Performance of Ordinal Classification.
  In International Journal of Pattern Recognition and Artificial Intelligence, 25(8):1173-1195, 2011.
  

See Also

fit, predict.fit, mining, mgraph, savemining and Importance.

Examples

### pure binary classification 
y=factor(c("a","a","a","a","b","b","b","b"))
x=factor(c("a","a","b","a","b","a","b","a"))
print(mmetric(y,x,"CONF")$conf)
print(mmetric(y,x,metric=c("ACC","TPR","ACCLASS")))
print(mmetric(y,x,"ALL"))

### probabilities binary classification 
y=factor(c("a","a","a","a","b","b","b","b"))
px=matrix(nrow=8,ncol=2)
px[,1]=c(1.0,0.9,0.8,0.7,0.6,0.5,0.4,0.3)
px[,2]=1-px[,1]
print(px)
print(mmetric(y,px,"CONF")$conf)
print(mmetric(y,px,"CONF",D=0.5,TC=2)$conf)
print(mmetric(y,px,"CONF",D=0.3,TC=2)$conf)
print(mmetric(y,px,metric="ALL",D=0.3,TC=2))
print(mmetric(y,px,metric=c("ACC","AUC","AUCCLASS","BRIER","BRIERCLASS","CE"),D=0.3,TC=2))
# ACC and confusion matrix:
print(mmetric(y,px,metric=c("ACC","CONF"),D=0.3,TC=2))
# ACC and ROC curve:
print(mmetric(y,px,metric=c("ACC","ROC"),D=0.3,TC=2))
# ACC, ROC and LIFT curve:
print(mmetric(y,px,metric=c("ACC","ROC","LIFT"),D=0.3,TC=2))

### pure multi-class classification 
y=c('A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A',
'A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A',
'A','A','B','B','B','B','B','B','B','B','B','B','C','C','C','C','C','C','C','C','C','C',
'C','C','C','C','C','D','D','D','D','D','D','D','D','D','D','D','D','D','D','D','D','D',
'D','D','D','D','D','D','D','D','E','E','E','E','E')
x=c('A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','A',
'A','A','A','A','A','A','A','A','A','A','A','A','A','A','E','E','E','E','E','D','D','D',
'D','D','B','B','B','B','B','B','B','B','B','D','C','C','C','C','C','C','C','B','B','B',
'B','B','C','C','C','D','D','D','D','D','D','D','D','D','D','D','D','D','D','D','D','D',
'D','D','D','D','D','D','C','C','E','A','A','B','B')
y=factor(y)
x=factor(x)
print(mmetric(y,x,metric="CONF")$conf) # confusion matrix
print(mmetric(y,x,metric="CONF",TC=-1)$conf) # same thing
print(mmetric(y,x,metric="CONF",TC=1)$conf) # for target class TC=1: "A"
mshow=function(y,x,metric) print(round(mmetric(y,x,metric),digits=0))
mshow(y,x,"ALL")
mshow(y,x,c("ACCLASS","BAL_ACC","KAPPA"))
mshow(y,x,c("PRECISION")) # precision
mshow(y,x,c("TPR")) # recall 
mshow(y,x,c("F1")) # F1 score

# micro (=ACC), macro and weighted average:
mshow(y,x,c("ACC","macroPRECISION","weightedPRECISION")) 
mshow(y,x,c("ACC","macroTPR","weightedTPR")) 
mshow(y,x,c("ACC","macroF1","weightedF1"))
mshow(y,x,c("ACC","macroACC","weightedACC"))

# several metrics in a single returned object:
print(mmetric(y,x,metric=c("CONF","macroF1","weightedF1","ACC")))

### probabilities multi-class 
y=factor(c("a","a","b","b","c","c"))
px=matrix(nrow=6,ncol=3)
px[,1]=c(1.0,0.7,0.5,0.3,0.1,0.7)
px[,2]=c(0.0,0.2,0.4,0.7,0.3,0.2)
px[,3]=1-px[,1]-px[,2]
print(px)
print(mmetric(y,px,metric="ALL",TC=-1,val=0.1))
print(mmetric(y,px,metric=c("AUC","AUCCLASS","NAUC"),TC=-1,val=0.1))
print(mmetric(y,px,metric=c("AUC","NAUC"),TC=3,val=0.1))
print(mmetric(y,px,metric=c("ACC","ACCLASS"),TC=-1))
print(mmetric(y,px,metric=c("CONF"),TC=3,D=0.5)$conf)
print(mmetric(y,px,metric=c("ACCLASS"),TC=3,D=0.5))
print(mmetric(y,px,metric=c("CONF"),TC=3,D=0.7)$conf)
print(mmetric(y,px,metric=c("ACCLASS"),TC=3,D=0.7))

### ordinal multi-class (example in Ricardo Sousa PhD thesis 2012)
y=ordered(c(rep("a",4),rep("b",6),rep("d",3)),levels=c("a","b","c","d"))
x=ordered(c(rep("c",(4+6)),rep("d",3)),levels=c("a","b","c","d"))
print(mmetric(y,x,metric="CONF")$conf)
print(mmetric(y,x,metric=c("CE","MAEO","MSEO","KENDALL")))
# note: only y needs to be ordered
x=factor(c(rep("b",4),rep("a",6),rep("d",3)),levels=c("a","b","c","d"))
print(mmetric(y,x,metric="CONF")$conf)
print(mmetric(y,x,metric=c("CE","MAEO","MSEO","KENDALL")))
print(mmetric(y,x,metric="ALL"))

### ranking (multi-class) 
y=matrix(nrow=1,ncol=12);x=y
# http://www.youtube.com/watch?v=D56dvoVrBBE
y[1,]=1:12
x[1,]=c(2,1,4,3,6,5,8,7,10,9,12,11)
print(mmetric(y,x,metric="KENDALL"))
print(mmetric(y,x,metric="ALL"))

y=matrix(nrow=2,ncol=7);x=y
y[1,]=c(2,6,5,4,3,7,1)
y[2,]=7:1
x[1,]=1:7
x[2,]=1:7
print(mmetric(y,x,metric="ALL"))

### regression examples: y - desired values; x - predictions
y=c(95.01,96.1,97.2,98.0,99.3,99.7);x=95:100
print(mmetric(y,x,"ALL"))
print(mmetric(y,x,"MAE"))
mshow=function(y,x,metric) print(round(mmetric(y,x,metric),digits=2))
mshow(y,x,c("MAE","RMSE","RAE","RSE"))
# getting NMAE:
m=mmetric(y,x,"NMAE")
cat("NMAE:",round(m,digits=2)," (denominator=",diff(range(y)),")\n")
m=mmetric(y,x,"NMAE",val=5) # usage of different range
cat("NMAE:",round(m,digits=2)," (denominator=",5,")\n")
# get REC curve and other measures:
m=mmetric(y,x,c("REC","TOLERANCEPERC","MAE"),val=5)
print(m)

# correlation or similar measures:
mshow(y,x,c("COR","R2","R22","EV")) # ideal is close to 1
mshow(y,x,c("q2","Q2")) # ideal is close to 0
# other measures:
print(mmetric(y,x,c("TOLERANCE","NAREC"),val=0.5)) # if admitted/accepted absolute error is 0.5
print(mmetric(y,x,"TOLERANCEPERC",val=0.05)) # tolerance for a 5% of yrange 
# tolerance for fixed 0.1 value and 5% of yrange:
print(mmetric(y,x,c("TOLERANCE","TOLERANCEPERC"),val=c(0.1,0.05))) 
print(mmetric(y,x,"THEILSU2",val=94.1)) # val = 1-ahead random walk, c(y,94.1), same as below
print(mmetric(y,x,"THEILSU2",val=c(94.1,y[1:5]))) # val = 1-ahead random walk (previous y values)
print(mmetric(y,x,"MASE",val=c(88.1,89.9,93.2,94.1))) # val = in-samples
val=vector("list",length=4)
val[[2]]=0.5;val[[3]]=94.1;val[[4]]=c(88.1,89.9,93.2,94.1)
print(mmetric(y,x,c("MAE","NAREC","THEILSU2","MASE"),val=val))
# user defined error function example:
# myerror = number of samples with absolute error above 0.1% of y: 
myerror=function(y,x){return (sum(abs(y-x)>(0.001*y)))}
print(mmetric(y,x,metric=myerror))
# example that returns a list since "REC" is included:
print(mmetric(y,x,c("MAE","REC","TOLERANCE","EV"),val=1))


### mining, several runs, prob multi-class
## Not run: 
data(iris)
M=mining(Species~.,iris,model="rpart",Runs=2)
R=mmetric(M,metric="CONF",aggregate="no")
print(R[[1]]$conf)
print(R[[2]]$conf)
print(mmetric(M,metric="CONF",aggregate="mean"))
print(mmetric(M,metric="CONF",aggregate="sum"))
print(mmetric(M,metric=c("ACC","ACCLASS"),aggregate="no"))
print(mmetric(M,metric=c("ACC","ACCLASS"),aggregate="mean"))
print(mmetric(M,metric="ALL",aggregate="no"))
print(mmetric(M,metric="ALL",aggregate="mean"))

## End(Not run)

### mining, several runs, regression
## Not run: 
data(sin1reg)
S=sample(1:nrow(sin1reg),40)
M=mining(y~.,data=sin1reg[S,],model="ksvm",search=2^3,Runs=10)
R=mmetric(M,metric="MAE")
print(mmetric(M,metric="MAE",aggregate="mean"))
miR=meanint(R) # mean and t-student confidence intervals
cat("MAE=",round(miR$mean,digits=2),"+-",round(miR$int,digits=2),"\n")
print(mmetric(M,metric=c("MAE","RMSE")))
print(mmetric(M,metric=c("MAE","RMSE"),aggregate="mean"))
R=mmetric(M,metric="REC",aggregate="no")
print(R[[1]]$rec)
print(mmetric(M,metric=c("TOLERANCE","NAREC"),val=0.2))
print(mmetric(M,metric=c("TOLERANCE","NAREC"),val=0.2,aggregate="mean"))

## End(Not run)

rminer documentation built on Oct. 29, 2024, 9:06 a.m.