Index

Symbols | A | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | W

Symbols

--arff_regression
skll_convert command line option
--arff_relation ARFF_RELATION
skll_convert command line option
--k <k>
print_model_weights command line option
--reuse_libsvm_map REUSE_LIBSVM_MAP
skll_convert command line option
--version
compute_eval_from_predictions command line option
filter_features command line option
generate_predictions command line option
join_features command line option
print_model_weights command line option
run_experiment command line option
skll_convert command line option
summarize_results command line option
-a <num_features>, --ablation <num_features>
run_experiment command line option
-a, --ablation
summarize_results command line option
-A, --ablation_all
run_experiment command line option
-f <feature <feature ...>>, --feature <feature <feature ...>>
filter_features command line option
-I <id <id ...>>, --id <id <id ...>>
filter_features command line option
-i, --inverse
filter_features command line option
-k, --keep-models
run_experiment command line option
-L <label <label ...>>, --label <label <label ...>>
filter_features command line option
-l <label_col>, --label_col <label_col>
generate_predictions command line option
join_features command line option
skll_convert command line option
-l label_col, --label_col label_col
filter_features command line option
-l, --local
run_experiment command line option
-m <machines>, --machines <machines>
run_experiment command line option
-p <positive_label>, --positive_label <positive_label>
generate_predictions command line option
-q <queue>, --queue <queue>
run_experiment command line option
-q, --quiet
filter_features command line option
generate_predictions command line option
join_features command line option
skll_convert command line option
-r, --resume
run_experiment command line option
-t <threshold>, --threshold <threshold>
generate_predictions command line option
-v, --verbose
run_experiment command line option

A

ARFFReader (class in skll.data.readers)
ARFFWriter (class in skll.data.writers)

C

compute_eval_from_predictions command line option
--version
examples_file
metric_names
predictions_file
cross_validate() (skll.Learner method)
(skll.learner.Learner method)
CSVReader (class in skll.data.readers)
CSVWriter (class in skll.data.writers)

D

DelimitedFileWriter (class in skll.data.writers)
DelimitedReader (class in skll.data.readers)
DictListReader (class in skll.data.readers)

E

evaluate() (skll.Learner method)
(skll.learner.Learner method)
examples_file
compute_eval_from_predictions command line option

F

f1_score_least_frequent() (in module skll)
(in module skll.metrics)
FeatureSet (class in skll)
(class in skll.data.featureset)
filter() (skll.data.featureset.FeatureSet method)
(skll.FeatureSet method)
filter_features command line option
--version
-I <id <id ...>>, --id <id <id ...>>
-L <label <label ...>>, --label <label <label ...>>
-f <feature <feature ...>>, --feature <feature <feature ...>>
-i, --inverse
-l label_col, --label_col label_col
-q, --quiet
infile
outfile
filtered_iter() (skll.data.featureset.FeatureSet method)
(skll.FeatureSet method)
FilteredLeaveOneGroupOut (class in skll.learner)
for_path() (skll.data.readers.Reader class method)
(skll.Reader class method)
(skll.Writer class method)
(skll.data.writers.Writer class method)
from_data_frame() (skll.data.featureset.FeatureSet static method)
(skll.FeatureSet static method)
from_file() (skll.Learner class method)
(skll.learner.Learner class method)

G

generate_predictions command line option
--version
-l <label_col>, --label_col <label_col>
-p <positive_label>, --positive_label <positive_label>
-q, --quiet
-t <threshold>, --threshold <threshold>
input_file
model_file

H

has_labels (skll.data.featureset.FeatureSet attribute)
(skll.FeatureSet attribute)

I

infile
filter_features command line option
skll_convert command line option
infile ...
join_features command line option
input_file
generate_predictions command line option

J

join_features command line option
--version
-l <label_col>, --label_col <label_col>
-q, --quiet
infile ...
outfile
json_file
summarize_results command line option

K

kappa() (in module skll)
(in module skll.metrics)
kendall_tau() (in module skll)
(in module skll.metrics)

L

Learner (class in skll)
(class in skll.learner)
learning_curve() (skll.Learner method)
(skll.learner.Learner method)
LibSVMReader (class in skll.data.readers)
LibSVMWriter (class in skll.data.writers)
load() (skll.Learner method)
(skll.learner.Learner method)

M

MegaMReader (class in skll.data.readers)
MegaMWriter (class in skll.data.writers)
metric_names
compute_eval_from_predictions command line option
model (skll.Learner attribute)
(skll.learner.Learner attribute)
model_file
generate_predictions command line option
print_model_weights command line option
model_kwargs (skll.Learner attribute)
(skll.learner.Learner attribute)
model_params (skll.Learner attribute)
(skll.learner.Learner attribute)
model_type (skll.Learner attribute)
(skll.learner.Learner attribute)

N

NDJReader (class in skll.data.readers)
NDJWriter (class in skll.data.writers)
NumpyTypeEncoder (class in skll.experiments)

O

outfile
filter_features command line option
join_features command line option
skll_convert command line option

P

pearson() (in module skll)
(in module skll.metrics)
predict() (skll.Learner method)
(skll.learner.Learner method)
predictions_file
compute_eval_from_predictions command line option
print_model_weights command line option
--k <k>
--version
model_file
sign {positive,negative,all}
probability (skll.Learner attribute)
(skll.learner.Learner attribute)

R

read() (skll.data.readers.Reader method)
(skll.Reader method)
Reader (class in skll)
(class in skll.data.readers)
rescaled() (in module skll.learner)
run_configuration() (in module skll)
(in module skll.experiments)
run_experiment command line option
--version
-A, --ablation_all
-a <num_features>, --ablation <num_features>
-k, --keep-models
-l, --local
-m <machines>, --machines <machines>
-q <queue>, --queue <queue>
-r, --resume
-v, --verbose

S

safe_float() (in module skll.data.readers)
save() (skll.Learner method)
(skll.learner.Learner method)
SelectByMinCount (class in skll.learner)
sign {positive,negative,all}
print_model_weights command line option
skll.data.featureset (module)
skll.data.readers (module)
skll.data.writers (module)
skll.experiments (module)
skll.learner (module)
skll.metrics (module)
skll_convert command line option
--arff_regression
--arff_relation ARFF_RELATION
--reuse_libsvm_map REUSE_LIBSVM_MAP
--version
-l <label_col>, --label_col <label_col>
-q, --quiet
infile
outfile
spearman() (in module skll)
(in module skll.metrics)
split_by_ids() (skll.data.featureset.FeatureSet static method)
(skll.FeatureSet static method)
split_with_quotes() (skll.data.readers.ARFFReader static method)
summarize_results command line option
--version
-a, --ablation
json_file
summary_file
summary_file
summarize_results command line option

T

train() (skll.Learner method)
(skll.learner.Learner method)
TSVReader (class in skll.data.readers)
TSVWriter (class in skll.data.writers)

U

use_score_func() (in module skll.metrics)

W

write() (skll.data.writers.Writer method)
(skll.Writer method)
Writer (class in skll)
(class in skll.data.writers)