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Uploaded 03-06-2016 by
Jan van Rijn
Weka_3.7.14-SNAPSHOT
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Weka implementation of MultiSearch
Components
W weka.IBk(9) Full name of base classifier.
(default: weka.classifiers.functions.LinearRegression)
Parameters
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built
(use with caution). C Do not try to eliminate colinear attributes. E Determines the parameter used for evaluation:
CC = Correlation coefficient
MCC = Matthews correlation coefficient
RMSE = Root mean squared error
RRSE = Root relative squared error
MAE = Mean absolute error
RAE = Root absolute error
COMB = Combined = (1-abs(CC)) + RRSE + RAE
ACC = Accuracy
KAP = Kappa
PREC = Precision
WPREC = Weighted precision
REC = Recall
WREC = Weighted recall
AUC = Area under ROC
WAUC = Weighted area under ROC
PRC = Area under PRC
WPRC = Weighted area under PRC
FM = F-Measure
WFM = Weighted F-Measure
TPR = True positive rate
TNR = True negative rate
FPR = False positive rate
FNR = False negative rate
(default: CC) default: CC R Set ridge parameter (default 1.0e-8). S Random number seed.
(default 1) default: 1 W Full name of base classifier.
(default: weka.classifiers.functions.LinearRegression) default: weka.classifiers.lazy.IBk additional-stats Output additional statistics. algorithm A search algorithm. default: weka.classifiers.meta.multisearch.DefaultSearch -sample-size 100.0 -initial-folds 2 -subsequent-folds 10 -initial-test-set . -subsequent-test-set . -num-slots 1 -num-slots 1 log-file The log file to log the messages to.
(default: none) default: /home/rijnjnvan/projects/OpenML/Java/OpenmlWeka minimal Conserve memory, don't keep dataset header and means/stdevs.
Model cannot be printed out if this option is enabled. (default: keep data) num-decimal-places The number of decimal places for the output of numbers in the model (default 2). output-debug-info If set, classifier is run in debug mode and
may output additional info to the console search A property search setup.
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Parameter:
none
-do-not-check-capabilities
C
E
R
S
W
additional-stats
algorithm
log-file
minimal
num-decimal-places
output-debug-info
search
Supervised Classification
Supervised Regression
Learning Curve
Supervised Data Stream Classification
Clustering
Machine Learning Challenge
Survival Analysis
Subgroup Discovery
area under roc curve
average cost
binominal test
build cpu time
build memory
c index
chi-squared
class complexity
class complexity gain
confusion matrix
correlation coefficient
cortana quality
coverage
f measure
information gain
jaccard
kappa
kb relative information score
kohavi wolpert bias squared
kohavi wolpert error
kohavi wolpert sigma squared
kohavi wolpert variance
kononenko bratko information score
matthews correlation coefficient
mean absolute error
mean class complexity
mean class complexity gain
mean f measure
mean kononenko bratko information score
mean precision
mean prior absolute error
mean prior class complexity
mean recall
mean weighted area under roc curve
mean weighted f measure
mean weighted precision
weighted recall
number of instances
os information
positives
precision
predictive accuracy
prior class complexity
prior entropy
probability
quality
ram hours
recall
relative absolute error
root mean prior squared error
root mean squared error
root relative squared error
run cpu time
run memory
run virtual memory
scimark benchmark
single point area under roc curve
total cost
unclassified instance count
usercpu time millis
usercpu time millis testing
usercpu time millis training
webb bias
webb error
webb variance
joint entropy
pattern team auroc10
wall clock time millis
wall clock time millis training
wall clock time millis testing
unweighted recall