R语言 rminer包 mmetric()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-9-27 00:02:23

mmetric(rminer)
mmetric()所属R语言包：rminer

 Compute classification or regression error metrics.
 计算分类或回归误差指标。

 译者：生物统计家园网机器人LoveR

描述----------Description----------

Compute classification or regression error metrics.
计算分类或回归误差指标。

用法----------Usage----------

mmetric(y, x = NULL, metric, D = 0.5, TC = -1, val = NULL)

参数----------Arguments----------

参数：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.
如果有预测（!is.null(x)）y应该是一个数值向量或与目标所需的响应（或输出值）。 其他因素，y应该是返回一个列表由mining功能。

参数：x
the predictions (should be a numeric vector if task="reg", matrix if task="prob" or factor if task="class" (use if y is not a list).
预测（应该是一个数值向量，如果task="reg"，矩阵task="prob"或因素，如果task="class"（使用y如果是不是一个列表）。

参数：metric
a R function or if a character valid options are:
一个R函数或字符有效的选项有：

AUC – overall area under the curve (of ROC curve, classification).
AUC - 总面积下的曲线（ROC曲线，分类）。

NAUC – normalized AUC (given a fixed val=FPR, classification).
NAUC - 归AUC（给定固定val=FPR分类）。

TPRATFPR – the TPR (given a fixed val=FPR, classification).
TPRATFPR - TPR（给定固定val=FPR分类）。

ALIFT – area of the accumulative percent of responses captured (LIFT accumulative curve, classification).
ALIFT - 面积的累计％的捕获（LIFT累计曲线，分类）的响应。

NALIFT – normalized ALIFT (given a fixed val=percentage of examples, classification).
NALIFT - 归一的A抬起（固定val=百分比的例子，分类）。

ALIFTATPERC – ALIFT value (given a fixed val=percentage of examples, classification).
ALIFTATPERC - 的A抬起价值（固定val=百分比的例子，分类）。

ACC – overall classification accuracy rate (classification).
ACC - 整体分类准确率（分类）。

BRIER – overall Brier score (for probabilistic classification).
BRIER - 整体的野蔷薇得分的概率分类。

CONF – confusion matrix (classification).
CONF - 混淆矩阵（分类）。

TPR – true positive rate (classification).
TPR - 真阳性率（分类）。

TNR – true negative rate (classification).
TNR - 真阴性率（分类）。

KAPPA – kappa index (classification).
KAPPA - Kappa指数（分类）。

SAD – sum absolute error (regression).
SAD - 和绝对误差（回归）。

MAD or MAE – mean absolute error (regression).
MAD或MAE - 平均绝对误差（回归）。

MdAE – median absolute error (regression).
MdAE - 平均绝对误差（回归）。

GMAE or GMAD – geometric mean absolute error (regression).
GMAE或GMAD - 几何平均绝对误差（回归）。

RMAD or RAE – relative absolute error (regression).
RMAD或RAE - 相对绝对误差（回归）。

SSE – sum squared error (regression).
SSE - 平方和错误（回归）。

MSE – mean squared error (regression).
MSE - 均方误差（回归）。

RSE – relative squared error (regression).
RSE - 相对误差（回归）。

RMSE – root mean squared error (regression).
“RMSE - 均方根误差（回归）。

ME – mean error (regression).
ME - 平均误差（回归）。

COR – correlation (regression).
COR - 相关（回归）。

R2 – R^2 (regression).
R2 - R ^ 2（回归）。

NAREC – normalized REC area (given a fixed val=tolerance, regression).
NAREC - 归REC区域（固定val=宽容，回归）。

TOLERANCE – the tolerance (y-axis value) of a REC curve (given a fixed val=tolerance, regression).
TOLERANCE - 公差（Y轴值）拍摄的曲线（固定val=宽容，回归）。

MdAPE – Mean Absolute Percentage mmetric forecasting metric (regression).
MdAPE - 平均绝对百分比mmetric预测的度量（回归）。

RMSPE – Root Mean Square Percentage mmetric forecasting metric (regression).
RMSPE - 根均方的百分比mmetric预测公制的（回归）。

RMdSPE – Root Median Square Percentage mmetric forecasting metric (regression).
RMdSPE - 根中位数广场百分比mmetric预测指标（回归）。

MAPE – Mean Absolute Percentage mmetric forecasting metric (regression).
MAPE - 平均绝对百分比mmetric预测的度量（回归）。

SMAPE – Symmetric Mean Absolute Percentage mmetric forecasting metric (regression).
SMAPE - 对称均值绝对百分比mmetric的预测度量（回归）。

SMdAPE – Symmetric Median Absolute Percentage mmetric forecasting metric (regression).

MRAE – Mean Relative Absolute mmetric forecasting metric (val includes the time series in-samples, or training data, regression).
MRAE - 平均的相对绝对mmetric预测度量（val包括时间序列的样本，培训资料，回归）。

MdRAE – Median Relative Absolute mmetric forecasting metric (val includes the time series in-samples, or training data, regression).
MdRAE - 中位数的相对绝对mmetric预测：公制（val包括时间序列的样本，培训资料，回归）。

GMRAE – Geometric Mean Relative Absoluate mmetric forecasting metric (val includes the time series in-samples, or training data, regression).
GMRAE - 几何平均相对Absoluate mmetric预测度量（val包括时间序列的样本，培训资料，回归）。

THEILSU2 – Theils'U2 forecasting metric (val includes the time series in-samples, or training data, regression).
THEILSU2 - TheilsU2预测度量（val包括时间序列的样本，培训资料，回归）。

MASE – MASE forecasting metric (val includes the time series in-samples, or training data, regression).
MASE - MASE预测公吨（val包括时间序列的样本，培训资料，回归）。

参数：D
decision threshold (for task="prob", probabilistic classification) within [0,1]. The class is TRUE if prob>D.
决策阈值（task="prob"，概率分类）在[0,1]。类是真实的，如果概率> D。

参数：TC
target class (for multi-class classification class) within 1,...,Nc, where Nc is the number of classes. Notes: if TC==-1 (the default value), then it is assumed:
目标多类分类的类内类1，...，NC，NC的班级数目。注：如果TC==-1（默认值），然后是假设：

if metric is "AUC", "ACC", "CONF", "KAPPA" or "BRIER" – the global metric (for all classes) is computed.
如果metric是“AUC”，“ACC”，“CONF”，“KAPPA”，“野蔷薇” - 全球度量（类）计算。

if metric is "ALIFTATPERC", "NALIFT", "LIFT", "NAUC", "TPRATFPR", "TPR" or "TNR" – TC is set to the index of the last class.
如果metric是“ALIFTATPERC的”，“LIFT”的“NALIFT”，中，“NAUC”中，“TPRATFPR”，“TPR”或“TNR” - TC·被设置为索引的最后一个类。

参数：val
auxiliary value, check the metric argument for details.
辅助值，检查metric参数的详细信息。

Details

详细信息----------Details----------

Compute classification or regression error metrics:
计算分类或回归误差指标：

mmetric – compute one error metric given y and x or given a mining list.
mmetric - 计算一个误差度量y和x或给出了采矿列表。

metrics – computes several classification or regression metrics. Additional arguments are:
metrics - 计算几个分类或回归指标。其他参数是：

AUC – if TRUE compute the AUC metrics.
AUC - 如果为true计算的AUC的指标。

BRIER – if TRUE compute the Brier score.
BRIER - 如果为true计算的蒺藜得分。

task –see fit.
task，看到fit。

In metrics, tauc is the global AUC and acc class is the each class accuracy. The metrics usage is: metrics(y, x, D = 0.5, TC = -1, AUC = TRUE, BRIER = FALSE, Run=1, task = "default") where:
在metrics，tauc是全球AUC和acc class是每级的精度。参考metrics的用法是：metrics(y, x, D = 0.5, TC = -1, AUC = TRUE, BRIER = FALSE, Run=1, task = "default") 其中：

Run – is the Run of mining execution if y is a mining object.
Run - “运行”采矿执行y是一个挖掘对象。

if AUC=TRUE or BRIER=TRUE then AUC/BRIER statistics are also computed.
如果AUC=TRUE或BRIER=TRUE那么AUC /野蔷薇统计的也计算。

</ul>
</ ul>

值----------Value----------

Returns the computed error metric(s).
返回的计算误差度量（S）。

注意----------Note----------

See also http://www3.dsi.uminho.pt/pcortez/rminer.html
也http://www3.dsi.uminho.pt/pcortez/rminer.html

（作者）----------Author(s)----------

Paulo Cortez <a href="http://www3.dsi.uminho.pt/pcortez">http://www3.dsi.uminho.pt/pcortez</a>

参考文献----------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: http://www.springerlink.com/content/e7u36014r04h0334 http://www3.dsi.uminho.pt/pcortez/2010-rminer.pdf 

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. http://dx.doi.org/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. </ul>

参见----------See Also----------

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

实例----------Examples----------

### regression[＃＃回归]
y=c(1:94,95.01,96.1,97.2,97.8,99.3,99.7);x=rnorm(100,5,0.3)+y
print(mmetric(y,x,"MAE"))
print(mmetric(y,x,"RMSE"))
print(mmetric(y,x,"TOLERANCE",val=5))
print(mmetric(y[95:100],x[95:100],"THEILSU2",val=y[1:94])) # b = in-samples[B =样本]
print(mmetric(y[95:100],x[95:100],"MASE",val=y[1:94])) # b = in-samples[B =样本]
print(metrics(y,x)) # several regression metrics[数回归指标]
# user defined error function example:[用户定义的误差函数的例子：]
# myerror = number of samples with absolute error above 10% of y: [myerror =与y的绝对误差在10％以上的样本数：]
myerror=function(y,x){return (sum(abs(y-x)>(0.1*y)))}
print(mmetric(y,x,metric=myerror))

### binary classification [＃＃二进制分类]
y=factor(c("a","a","b","b"))
x=factor(c("a","b","b","b"))
print(mmetric(y,x,"CONF"))
print(mmetric(y,x,"ACC"))
print(metrics(y,x))
px=matrix(nrow=4,ncol=2)
px[1,]=c(0.7,0.3)
px[2,]=c(0.4,0.6)
px[3,]=c(0.7,0.3)
px[4,]=c(0.3,0.7)
print(px)
print(mmetric(y,px,"CONF"))
print(mmetric(y,px,"ACC"))
print(mmetric(y,px,"CONF",D=0.7,TC=2))
print(metrics(y,px,D=0.7,TC=2))
px2=px[,2]
print(px2)
print(mmetric(y,px,"CONF"))
print(mmetric(y,px2,"CONF",D=0.7,TC=2))
print(mmetric(y,px,"AUC"))
print(mmetric(y,px2,"AUC"))
print(mmetric(y,px2,"AUC",TC=2))

### multi-class classification [＃＃多类分类]
data(iris)
M=mining(Species~.,iris,model="dt",Runs=2)
print(mmetric(M,metric="ACC",TC=2,D=0.7))
print(mmetric(M,metric="CONF",TC=2,D=0.7))
print(mmetric(M,metric="AUC",TC=3))
print(mmetric(M,metric="AUC",TC=1))
print(mmetric(M,metric="TPR",TC=1))
print(mmetric(M,metric="TPRATFPR",TC=3,val=0.05))
print(mmetric(M,metric="NAUC",TC=3,val=0.05))
print(mmetric(M,metric="ALIFT",TC=3))
print(mmetric(M,metric="ALIFTATPERC",TC=3,val=0.1))
print(mmetric(M,metric="NALIFT",TC=3,val=0.1))
print(metrics(M,BRIER=TRUE,Run=1)) # several Run 1 classification metrics[几个运行1分类指标]
print(metrics(M,BRIER=TRUE,Run=1,TC=3)) # several Run 1 TC=3 classification metrics[几个运行1 TC = 3类指标]

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