Index
Symbols
|
_
|
A
|
B
|
C
|
D
|
E
|
F
|
G
|
H
|
I
|
K
|
L
|
M
|
N
|
O
|
P
|
R
|
S
|
T
|
U
|
V
|
W
|
X
|
Y
|
Z
Symbols
.. index:: linear fitter
_
__bool__() (Orange.data.sql.table.SqlTable method)
__contains__() (Orange.data.Domain method)
__getitem__() (in module Orange.data.storage)
(Orange.data.Domain method)
(Orange.data.sql.table.SqlTable method)
__init__() (Orange.data.Domain method)
(Orange.data.sql.table.SqlTable method)
__iter__() (Orange.data.sql.table.SqlTable method)
__len__() (Orange.data.Domain method)
(Orange.data.sql.table.SqlTable method)
(Orange.data.storage. method)
__new__() (Orange.data.sql.table.SqlTable static method)
_compute_contingency() (Orange.data.storage.Storage method)
_compute_distributions() (in module Orange.data.storage)
_filter_has_class() (in module Orange.data.storage)
_filter_is_defined() (in module Orange.data.storage)
_filter_same_value() (in module Orange.data.storage)
_filter_values() (in module Orange.data.storage)
A
actual (Orange.evaluation.testing.Results attribute)
adjust_decimals (Orange.data.ContinuousVariable attribute)
anonymous (Orange.data.Domain attribute)
ANOVA (class in Orange.preprocess.score)
attributes (Orange.data.Domain attribute)
(Orange.data.Variable attribute)
attributes() (Orange.data.Instance method)
AUC
AUC() (in module Orange.evaluation)
axis (Orange.distance.Distance attribute)
(Orange.misc.distmatrix.DistMatrix attribute)
B
base_leaner (Orange.classification.calibration.ThresholdLearner attribute)
base_learner (Orange.classification.calibration.CalibratedLearner attribute)
base_model (Orange.classification.calibration.CalibratedClassifier attribute)
(Orange.classification.calibration.ThresholdClassifier attribute)
C
CA
CA() (in module Orange.evaluation)
ca() (Orange.evaluation.performance_curves.Curves method)
CalibratedClassifier (class in Orange.classification.calibration)
CalibratedLearner (class in Orange.classification.calibration)
calibration_method (Orange.classification.calibration.CalibratedLearner attribute)
calibrators (Orange.classification.calibration.CalibratedClassifier attribute)
case_sensitive (Orange.data.filter.FilterString attribute)
(Orange.data.filter.FilterStringList attribute)
CatGBClassifier (class in Orange.classification.catgb)
CatGBRegressor (class in Orange.regression.catgb)
checksum() (Orange.data.sql.table.SqlTable method)
(Orange.data.Table method)
Chi2 (class in Orange.preprocess.score)
class_type (Orange.preprocess.score.ANOVA attribute)
(Orange.preprocess.score.Chi2 attribute)
(Orange.preprocess.score.FCBF attribute)
(Orange.preprocess.score.GainRatio attribute)
(Orange.preprocess.score.Gini attribute)
(Orange.preprocess.score.InfoGain attribute)
(Orange.preprocess.score.ReliefF attribute)
(Orange.preprocess.score.RReliefF attribute)
(Orange.preprocess.score.UnivariateLinearRegression attribute)
class_var (Orange.data.Domain attribute)
class_vars (Orange.data.Domain attribute)
classes() (Orange.data.Instance method)
classification
accuracy
area under ROC
classifier
elliptic envelope
isolation forest
k-nearest neighbors
,
[1]
learner
linear SVM
local outlier factor
logistic regression
,
[1]
,
[2]
majority
naive Bayes
neural network
Nu-SVM
one class SVM
random forest
rules
scoring
simple random forest
simple tree
softmax regression
SVM
tree
trees
classification tree
(simple)
clustering
hierarchical clustering
CN2Learner (class in Orange.classification.rules)
CN2SDLearner (class in Orange.classification.rules)
CN2SDUnorderedLearner (class in Orange.classification.rules)
CN2UnorderedLearner (class in Orange.classification.rules)
col_items (Orange.misc.distmatrix.DistMatrix attribute)
column (Orange.data.filter.FilterContinuous attribute)
(Orange.data.filter.FilterDiscrete attribute)
(Orange.data.filter.FilterString attribute)
(Orange.data.filter.FilterStringList attribute)
(Orange.data.filter.SameValue attribute)
columns (Orange.data.filter.IsDefined attribute)
(Orange.data.Table property)
compute() (Orange.data.util.SharedComputeValue method)
compute_value (Orange.data.Variable attribute)
conditions (Orange.data.filter.Values attribute)
conjunction (Orange.data.filter.Values attribute)
connection (Orange.data.sql.table.SqlTable attribute)
ContinuousVariable (class in Orange.data)
copy() (Orange.data.sql.table.SqlTable method)
cross-validation
CrossValidation (class in Orange.evaluation.testing)
CrossValidationFeature (class in Orange.evaluation.testing)
CurveFitLearner (class in Orange.regression.curvefit)
Curves (class in Orange.evaluation.performance_curves)
D
Data
data
attributes
class
domain
examples
input
instances
missing values
preprocessing
sampling
data (Orange.evaluation.testing.Results attribute)
data mining
supervised
database (Orange.data.sql.table.SqlTable attribute)
dim (Orange.misc.distmatrix.DistMatrix property)
DiscreteVariable (class in Orange.data)
Discretization (class in Orange.preprocess.discretize)
discretize data
Distance (class in Orange.distance)
DistMatrix (class in Orange.misc.distmatrix)
Domain (class in Orange.data)
domain (in module Orange.data.storage)
(Orange.data.Instance property)
(Orange.data.Table attribute)
download_data() (Orange.data.sql.table.SqlTable method)
E
elliptic envelope
classification
EllipticEnvelopeLearner (class in Orange.classification)
ensure_copy() (Orange.data.Table method)
EntropyMDL (class in Orange.preprocess.discretize)
EqualFreq (class in Orange.preprocess.discretize)
EqualWidth (class in Orange.preprocess.discretize)
F
F1
F1() (in module Orange.evaluation)
f1() (Orange.evaluation.performance_curves.Curves method)
FCBF (class in Orange.preprocess.score)
feature
discretize
selection
feature (Orange.evaluation.testing.CrossValidationFeature attribute)
feature_type (Orange.preprocess.score.ANOVA attribute)
(Orange.preprocess.score.Chi2 attribute)
(Orange.preprocess.score.FCBF attribute)
(Orange.preprocess.score.GainRatio attribute)
(Orange.preprocess.score.Gini attribute)
(Orange.preprocess.score.InfoGain attribute)
(Orange.preprocess.score.ReliefF attribute)
(Orange.preprocess.score.RReliefF attribute)
(Orange.preprocess.score.UnivariateLinearRegression attribute)
FilterContinuous (class in Orange.data.filter)
FilterDiscrete (class in Orange.data.filter)
FilterRegex (class in Orange.data.filter)
FilterString (class in Orange.data.filter)
FilterStringList (class in Orange.data.filter)
fit_storage() (Orange.classification.calibration.CalibratedLearner method)
(Orange.classification.calibration.ThresholdLearner method)
(Orange.classification.MajorityLearner method)
(Orange.classification.NaiveBayesLearner method)
(Orange.classification.rules.CN2Learner method)
(Orange.classification.rules.CN2SDLearner method)
(Orange.classification.rules.CN2SDUnorderedLearner method)
(Orange.classification.rules.CN2UnorderedLearner method)
(Orange.classification.SimpleRandomForestLearner method)
(Orange.classification.SimpleTreeLearner method)
(Orange.classification.TreeLearner method)
(Orange.regression.curvefit.CurveFitLearner method)
(Orange.regression.MeanLearner method)
(Orange.regression.SimpleRandomForestLearner method)
(Orange.regression.TreeLearner method)
flat (Orange.misc.distmatrix.DistMatrix property)
fn (Orange.evaluation.performance_curves.Curves attribute)
folds (Orange.evaluation.testing.Results attribute)
force (Orange.preprocess.discretize.EntropyMDL attribute)
fp (Orange.evaluation.performance_curves.Curves attribute)
fpr() (Orange.evaluation.performance_curves.Curves method)
FreeViz (class in Orange.projection.freeviz)
from_domain() (Orange.data.Table class method)
from_file() (Orange.data.Table class method)
(Orange.misc.distmatrix.DistMatrix class method)
from_numpy() (Orange.data.Domain class method)
(Orange.data.Table class method)
from_results() (Orange.evaluation.performance_curves.Curves class method)
from_table() (Orange.data.sql.table.SqlTable class method)
(Orange.data.Table class method)
from_table_rows() (Orange.data.Table class method)
G
GainRatio (class in Orange.preprocess.score)
GBClassifier (class in Orange.classification.gb)
GBRegressor (class in Orange.regression.gb)
get_augmented_data() (Orange.evaluation.testing.Results method)
get_class() (Orange.data.Instance method)
get_classes() (Orange.data.Instance method)
get_indices() (Orange.evaluation.testing.CrossValidation method)
(Orange.evaluation.testing.CrossValidationFeature method)
(Orange.evaluation.testing.LeaveOneOut method)
(Orange.evaluation.testing.ShuffleSplit method)
Gini (class in Orange.preprocess.score)
H
has_col_labels() (Orange.misc.distmatrix.DistMatrix method)
has_continuous_attributes() (Orange.data.Domain method)
has_discrete_attributes() (Orange.data.Domain method)
has_missing() (Orange.data.Table method)
has_missing_class() (Orange.data.Table method)
has_row_labels() (Orange.misc.distmatrix.DistMatrix method)
has_weights() (Orange.data.sql.table.SqlTable method)
(Orange.data.Table method)
HasClass (class in Orange.data.filter)
hierarchical clustering
clustering
HierarchicalClustering (class in Orange.clustering.hierarchical)
host (Orange.data.sql.table.SqlTable attribute)
I
ids (Orange.data.sql.table.SqlTable property)
impute (Orange.distance.Distance attribute)
IncrementalPCA (class in Orange.projection.pca)
index() (Orange.data.Domain method)
InfoGain (class in Orange.preprocess.score)
Instance (class in Orange.data)
is_primitive() (Orange.data.ContinuousVariable class method)
(Orange.data.DiscreteVariable class method)
(Orange.data.StringVariable class method)
(Orange.data.Variable class method)
IsDefined (class in Orange.data.filter)
isolation forest
classification
IsolationForestLearner (class in Orange.classification)
K
k (Orange.evaluation.testing.CrossValidation attribute)
k-nearest neighbors
classification
k-nearest neighbors classifier
KNNLearner (class in Orange.classification)
L
LassoRegressionLearner (class in Orange.regression.linear)
LDA (class in Orange.projection.lda)
LeaveOneOut (class in Orange.evaluation.testing)
linear
linear fitter
regression
linear SVM
classification
LinearModel (class in Orange.regression.linear)
LinearRegressionLearner (class in Orange.regression.linear)
LinearSVMLearner (class in Orange.classification)
list (Orange.data.Instance property)
local outlier factor
classification
LocalOutlierFactorLearner (class in Orange.classification)
Log loss
logistic regression
classification
LogisticRegressionLearner (class in Orange.classification)
LogLoss() (in module Orange.evaluation)
M
MAE
MAE() (in module Orange.evaluation)
majority
classification
majority classifier
MajorityLearner (class in Orange.classification)
make() (Orange.data.ContinuousVariable class method)
(Orange.data.DiscreteVariable class method)
(Orange.data.StringVariable class method)
max (Orange.data.filter.FilterContinuous attribute)
(Orange.data.filter.FilterString attribute)
mean fitter
regression
MeanLearner (class in Orange.regression)
metas (Orange.data.Domain attribute)
(Orange.data.Instance property)
(Orange.data.sql.table.SqlTable property)
models (Orange.evaluation.testing.Results attribute)
module
Orange.classification
Orange.classification.calibration
Orange.classification.catgb
Orange.classification.gb
Orange.classification.rules
Orange.classification.xgb
Orange.clustering
Orange.data.filter
Orange.data.variable
Orange.evaluation
Orange.evaluation.testing
Orange.misc
Orange.misc.distmatrix
Orange.projection
Orange.regression
Orange.regression.catgb
Orange.regression.curvefit
Orange.regression.gb
Orange.regression.xgb
MSE
MSE() (in module Orange.evaluation)
multinomial_treatment (Orange.preprocess.Orange.preprocess.Continuize attribute)
N
n (Orange.evaluation.performance_curves.Curves attribute)
(Orange.preprocess.discretize.EqualFreq attribute)
(Orange.preprocess.discretize.EqualWidth attribute)
n_resamples (Orange.evaluation.testing.ShuffleSplit attribute)
naive Bayes
classification
naive Bayes classifier
NaiveBayesLearner (class in Orange.classification)
name (Orange.data.Variable attribute)
negate (Orange.data.filter.Values attribute)
neural network
,
[1]
classification
regression
NNClassificationLearner (class in Orange.classification)
NNRegressionLearner (class in Orange.regression)
Normalize (class in Orange.preprocess)
normalize (Orange.distance.Distance attribute)
npv() (Orange.evaluation.performance_curves.Curves method)
nrows (Orange.evaluation.testing.Results attribute)
Nu-SVM
classification
number_of_decimals (Orange.data.ContinuousVariable attribute)
NuSVMLearner (class in Orange.classification)
O
one class SVM
classification
OneClassSVMLearner (class in Orange.classification)
oper (Orange.data.filter.FilterContinuous attribute)
(Orange.data.filter.FilterString attribute)
Orange.classification
module
Orange.classification.calibration
module
Orange.classification.catgb
module
Orange.classification.gb
module
Orange.classification.rules
module
Orange.classification.xgb
module
Orange.clustering
module
Orange.data.filter
module
Orange.data.variable
module
Orange.evaluation
module
Orange.evaluation.testing
module
Orange.misc
module
Orange.misc.distmatrix
module
Orange.preprocess.Continuize (class in Orange.preprocess)
Orange.preprocess.DomainContinuizer (class in Orange.preprocess)
Orange.projection
module
Orange.regression
module
Orange.regression.catgb
module
Orange.regression.curvefit
module
Orange.regression.gb
module
Orange.regression.xgb
module
P
p (Orange.evaluation.performance_curves.Curves attribute)
parse() (Orange.data.TimeVariable method)
PCA (class in Orange.projection.pca)
PolynomialLearner (class in Orange.regression.linear)
ppv() (Orange.evaluation.performance_curves.Curves method)
Precision
Precision() (in module Orange.evaluation)
precision() (Orange.evaluation.performance_curves.Curves method)
PrecisionRecallFSupport
PrecisionRecallFSupport() (in module Orange.evaluation)
predicted (Orange.evaluation.testing.Results attribute)
prepare_arrays() (Orange.evaluation.testing.LeaveOneOut static method)
preprocessing
preprocessors (Orange.classification.LinearSVMLearner attribute)
(Orange.classification.LogisticRegressionLearner attribute)
(Orange.classification.NaiveBayesLearner attribute)
(Orange.classification.NuSVMLearner attribute)
(Orange.classification.OneClassSVMLearner attribute)
(Orange.classification.SoftmaxRegressionLearner attribute)
(Orange.classification.SVMLearner attribute)
(Orange.regression.curvefit.CurveFitLearner attribute)
prob (Orange.data.filter.Random attribute)
probabilities (Orange.evaluation.testing.Results attribute)
probs (Orange.evaluation.performance_curves.Curves attribute)
R
R2
R2() (in module Orange.evaluation)
Random (class in Orange.data.filter)
random forest
,
[1]
classification
regression
random forest (simple)
,
[1]
random_state (Orange.evaluation.testing.CrossValidation attribute)
(Orange.evaluation.testing.ShuffleSplit attribute)
RandomForestLearner (class in Orange.classification)
RandomForestRegressionLearner (class in Orange.regression)
Randomize (class in Orange.preprocess)
Recall
Recall() (in module Orange.evaluation)
recall() (Orange.evaluation.performance_curves.Curves method)
ref (Orange.data.filter.FilterContinuous attribute)
(Orange.data.filter.FilterString attribute)
regression
linear
linear fitter
mean fitter
neural network
random forest
simple random forest
tree
,
[1]
regression tree
ReliefF (class in Orange.preprocess.score)
Remove (class in Orange.preprocess)
Results (class in Orange.evaluation.testing)
RidgeRegressionLearner (class in Orange.regression.linear)
row_filters (Orange.data.sql.table.SqlTable attribute)
row_indices (Orange.evaluation.testing.Results attribute)
row_items (Orange.misc.distmatrix.DistMatrix attribute)
RowInstance (class in Orange.data)
RReliefF (class in Orange.preprocess.score)
Rule induction
rules
classification
S
SameValue (class in Orange.data.filter)
sample() (in module Orange.evaluation.testing)
SelectBestFeatures (class in Orange.preprocess)
sensitivity() (Orange.evaluation.performance_curves.Curves method)
set_class() (Orange.data.Instance method)
(Orange.data.RowInstance method)
set_weights() (Orange.data.Table method)
SGDRegressionLearner (class in Orange.regression.linear)
SharedComputeValue (class in Orange.data.util)
shuffle() (Orange.data.Table method)
ShuffleSplit (class in Orange.evaluation.testing)
simple random forest
classification
regression
simple tree
classification
SimpleRandomForestLearner (class in Orange.classification)
(class in Orange.regression)
SimpleTreeLearner (class in Orange.classification)
SklTreeLearner (class in Orange.classification)
SklTreeRegressionLearner (class in Orange.regression)
softmax regression
classification
softmax regression classifier
SoftmaxRegressionLearner (class in Orange.classification)
source_variable (Orange.data.Variable attribute)
sparse (Orange.data.Variable attribute)
SparsePCA (class in Orange.projection.pca)
specificity() (Orange.evaluation.performance_curves.Curves method)
split_by_model() (Orange.evaluation.testing.Results method)
SqlTable (class in Orange.data.sql.table)
str_val() (Orange.data.ContinuousVariable method)
(Orange.data.DiscreteVariable method)
(Orange.data.StringVariable static method)
(Orange.data.Variable static method)
stratified (Orange.evaluation.testing.CrossValidation attribute)
(Orange.evaluation.testing.ShuffleSplit attribute)
StringVariable (class in Orange.data)
submatrix() (Orange.misc.distmatrix.DistMatrix method)
SVM
,
[1]
classification
SVMLearner (class in Orange.classification)
T
Table (class in Orange.data)
table_name (Orange.data.sql.table.SqlTable attribute)
test_size (Orange.evaluation.testing.ShuffleSplit attribute)
test_time (Orange.evaluation.testing.Results attribute)
TestOnTestData (class in Orange.evaluation.testing)
TestOnTrainingData (class in Orange.evaluation.testing)
threshold (Orange.classification.calibration.ThresholdClassifier attribute)
threshold_criterion (Orange.classification.calibration.ThresholdLearner attribute)
ThresholdClassifier (class in Orange.classification.calibration)
ThresholdLearner (class in Orange.classification.calibration)
TimeVariable (class in Orange.data)
tn (Orange.evaluation.performance_curves.Curves attribute)
to_val() (Orange.data.ContinuousVariable method)
(Orange.data.DiscreteVariable method)
(Orange.data.StringVariable method)
(Orange.data.Variable method)
tot (Orange.evaluation.performance_curves.Curves attribute)
total_weight() (Orange.data.Table method)
tp (Orange.evaluation.performance_curves.Curves attribute)
tpr() (Orange.evaluation.performance_curves.Curves method)
train_size (Orange.evaluation.testing.ShuffleSplit attribute)
train_time (Orange.evaluation.testing.Results attribute)
transform_class (Orange.preprocess.Orange.preprocess.Continuize attribute)
tree
classification
regression
TreeLearner (class in Orange.classification)
(class in Orange.regression)
Type (Orange.data.filter.FilterContinuous attribute)
(Orange.data.filter.FilterString attribute)
U
UnivariateLinearRegression (class in Orange.preprocess.score)
unknown_str (Orange.data.Variable attribute)
unlocked() (Orange.data.sql.table.SqlTable method)
V
val_from_str_add() (Orange.data.ContinuousVariable method)
(Orange.data.DiscreteVariable method)
(Orange.data.StringVariable method)
(Orange.data.Variable method)
Value (class in Orange.data.variable)
value (Orange.data.filter.SameValue attribute)
(Orange.data.variable.Value attribute)
Values (class in Orange.data.filter)
values (Orange.data.DiscreteVariable attribute)
(Orange.data.filter.FilterDiscrete attribute)
(Orange.data.filter.FilterStringList attribute)
Variable (class in Orange.data)
variable (Orange.data.variable.Value attribute)
variables (Orange.data.Domain attribute)
W
W (Orange.data.sql.table.SqlTable property)
weight (Orange.data.Instance property)
(Orange.data.RowInstance property)
X
x (Orange.data.Instance property)
X (Orange.data.sql.table.SqlTable property)
XGBClassifier (class in Orange.classification.xgb)
XGBRegressor (class in Orange.regression.xgb)
XGBRFClassifier (class in Orange.classification.xgb)
XGBRFRegressor (class in Orange.regression.xgb)
Y
y (Orange.data.Instance property)
Y (Orange.data.sql.table.SqlTable property)
ytrue (Orange.evaluation.performance_curves.Curves attribute)
Z
zero_based (Orange.preprocess.Orange.preprocess.Continuize attribute)
Orange Data Mining Library
Navigation
The Data
Classification
Regression
Data model (
data
)
Data Preprocessing (
preprocess
)
Outlier detection (
classification
)
Classification (
classification
)
Regression (
regression
)
Clustering (
clustering
)
Distance (
distance
)
Evaluation (
evaluation
)
Projection (
projection
)
Miscellaneous (
misc
)
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