pandas_ml
stable
What’s new
Data Handling
Use scikit-learn
Handling imbalanced data
Use XGBoost
Use patsy
Confusion matrix
pandas_ml.core package
pandas_ml.skaccessors package
pandas_ml.xgboost package
pandas_ml
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Index
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B
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C
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D
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E
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F
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G
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H
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I
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J
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K
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L
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M
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N
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O
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P
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Q
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R
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S
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T
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U
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V
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X
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Y
A
ACC (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
add_dummy_feature() (pandas_ml.skaccessors.preprocessing.PreprocessingMethods method)
affinity_propagation() (pandas_ml.skaccessors.cluster.ClusterMethods method)
assert_numpy_array_almost_equal() (pandas_ml.util.testing.TestCase method)
assertAlmostEqual() (pandas_ml.util.testing.TestCase method)
assertEqual() (pandas_ml.util.testing.TestCase method)
assertFalse() (pandas_ml.util.testing.TestCase method)
assertIs() (pandas_ml.util.testing.TestCase method)
assertIsInstance() (pandas_ml.util.testing.TestCase method)
assertIsNone() (pandas_ml.util.testing.TestCase method)
assertTrue() (pandas_ml.util.testing.TestCase method)
auc() (pandas_ml.skaccessors.metrics.MetricsMethods method)
average_precision_score() (pandas_ml.skaccessors.metrics.MetricsMethods method)
B
bicluster (pandas_ml.skaccessors.cluster.ClusterMethods attribute)
binarize() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
BinaryConfusionMatrix (class in pandas_ml.confusion_matrix.bcm)
binom_interval() (in module pandas_ml.confusion_matrix.stats)
Bunch (class in pandas_ml.skaccessors.base)
C
calibration (pandas_ml.core.frame.ModelFrame attribute)
check_cv() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
check_increasing() (pandas_ml.skaccessors.isotonic.IsotonicMethods method)
choose() (in module pandas_ml.confusion_matrix.stats)
class_agreement() (in module pandas_ml.confusion_matrix.stats)
classes (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
classification_report (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
cls (pandas_ml.core.frame.ModelFrame attribute)
cluster (pandas_ml.core.frame.ModelFrame attribute)
clustermap() (pandas_ml.snsaccessors.base.SeabornMethods method)
ClusterMethods (class in pandas_ml.skaccessors.cluster)
coefplot() (pandas_ml.snsaccessors.base.SeabornMethods method)
combine (pandas_ml.imbaccessors.base.ImbalanceMethods attribute)
confusion_matrix() (pandas_ml.skaccessors.metrics.MetricsMethods method)
ConfusionMatrix (class in pandas_ml.confusion_matrix.cm)
ConfusionMatrixAbstract (class in pandas_ml.confusion_matrix.abstract)
consensus_score() (pandas_ml.skaccessors.metrics.MetricsMethods method)
correlation_models (pandas_ml.skaccessors.gaussian_process.GaussianProcessMethods attribute)
countplot() (pandas_ml.snsaccessors.base.SeabornMethods method)
covariance (pandas_ml.core.frame.ModelFrame attribute)
CovarianceMethods (class in pandas_ml.skaccessors.covariance)
cross_decomposition (pandas_ml.core.frame.ModelFrame attribute)
cross_val_score() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
CrossDecompositionMethods (class in pandas_ml.skaccessors.cross_decomposition)
D
da (pandas_ml.core.frame.ModelFrame attribute)
data (pandas_ml.core.frame.ModelFrame attribute)
dbscan() (pandas_ml.skaccessors.cluster.ClusterMethods method)
decision (pandas_ml.core.generic.ModelPredictor attribute)
decision_function() (pandas_ml.core.frame.ModelFrame method)
decomposition (pandas_ml.core.frame.ModelFrame attribute)
DecompositionMethods (class in pandas_ml.skaccessors.decomposition)
describe() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
dict_class() (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix method)
dict_learning() (pandas_ml.skaccessors.decomposition.DecompositionMethods method)
dict_learning_online() (pandas_ml.skaccessors.decomposition.DecompositionMethods method)
discriminant_analysis (pandas_ml.core.frame.ModelFrame attribute)
distplot() (pandas_ml.snsaccessors.base.SeabornMethods method)
DOR (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
dummy (pandas_ml.core.frame.ModelFrame attribute)
E
empirical_covariance() (pandas_ml.skaccessors.covariance.CovarianceMethods method)
enet_path() (pandas_ml.skaccessors.linear_model.LinearModelMethods method)
enlarge() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
ensemble (pandas_ml.core.frame.ModelFrame attribute)
(pandas_ml.imbaccessors.base.ImbalanceMethods attribute)
EnsembleMethods (class in pandas_ml.skaccessors.ensemble)
estimator (pandas_ml.core.generic.ModelPredictor attribute)
F
F1_score (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
f1_score() (pandas_ml.skaccessors.metrics.MetricsMethods method)
FacetGrid() (pandas_ml.snsaccessors.base.SeabornMethods method)
fastica() (pandas_ml.skaccessors.decomposition.DecompositionMethods method)
fbeta_score() (pandas_ml.skaccessors.metrics.MetricsMethods method)
FDR (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
feature_extraction (pandas_ml.core.frame.ModelFrame attribute)
feature_selection (pandas_ml.core.frame.ModelFrame attribute)
FeatureExtractionMethods (class in pandas_ml.skaccessors.feature_extraction)
FeatureSelectionMethods (class in pandas_ml.skaccessors.feature_selection)
fit() (pandas_ml.core.generic.ModelTransformer method)
(pandas_ml.smaccessors.base.StatsModelsRegressor method)
fit_predict() (pandas_ml.core.frame.ModelFrame method)
fit_resample() (pandas_ml.core.frame.ModelFrame method)
fit_sample() (pandas_ml.core.frame.ModelFrame method)
fit_transform() (pandas_ml.core.frame.ModelFrame method)
(pandas_ml.core.generic.ModelTransformer method)
FN (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
FNR (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
FOR (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
format() (pandas_ml.util.testing.TestCase method)
format_values() (pandas_ml.util.testing.TestCase method)
FP (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
FPR (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
G
gaussian_process (pandas_ml.core.frame.ModelFrame attribute)
GaussianProcessMethods (class in pandas_ml.skaccessors.gaussian_process)
get() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
get_params() (pandas_ml.smaccessors.base.StatsModelsRegressor method)
gp (pandas_ml.core.frame.ModelFrame attribute)
groupby() (in module pandas_ml.core.groupby)
(pandas_ml.core.frame.ModelFrame method)
(pandas_ml.core.series.ModelSeries method)
GroupedEstimator (class in pandas_ml.core.groupby)
H
has_data() (pandas_ml.core.frame.ModelFrame method)
has_multi_targets() (pandas_ml.core.frame.ModelFrame method)
has_target() (pandas_ml.core.frame.ModelFrame method)
heatmap() (pandas_ml.snsaccessors.base.SeabornMethods method)
help() (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix class method)
hinge_loss() (pandas_ml.skaccessors.metrics.MetricsMethods method)
hit (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
I
image (pandas_ml.skaccessors.feature_extraction.FeatureExtractionMethods attribute)
imbalance (pandas_ml.core.frame.ModelFrame attribute)
ImbalanceMethods (class in pandas_ml.imbaccessors.base)
info() (in module pandas_ml.tools)
informedness (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
interactplot() (pandas_ml.snsaccessors.base.SeabornMethods method)
inverse() (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix method)
inverse_transform() (pandas_ml.core.frame.ModelFrame method)
(pandas_ml.core.generic.ModelTransformer method)
is_binary (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
(pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
isotonic (pandas_ml.core.frame.ModelFrame attribute)
isotonic_regression() (pandas_ml.skaccessors.isotonic.IsotonicMethods method)
IsotonicMethods (class in pandas_ml.skaccessors.isotonic)
IsotonicRegression (pandas_ml.skaccessors.isotonic.IsotonicMethods attribute)
iterate() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
J
JointGrid() (pandas_ml.snsaccessors.base.SeabornMethods method)
K
k_means() (pandas_ml.skaccessors.cluster.ClusterMethods method)
kdeplot() (pandas_ml.snsaccessors.base.SeabornMethods method)
kernel_approximation (pandas_ml.core.frame.ModelFrame attribute)
kernel_ridge (pandas_ml.core.frame.ModelFrame attribute)
L
l1_min_c() (pandas_ml.skaccessors.svm.SVMMethods method)
LabeledConfusionMatrix (class in pandas_ml.confusion_matrix.cm)
lars_path() (pandas_ml.skaccessors.linear_model.LinearModelMethods method)
lasso_path() (pandas_ml.skaccessors.linear_model.LinearModelMethods method)
lasso_stability_path() (pandas_ml.skaccessors.linear_model.LinearModelMethods method)
lda (pandas_ml.core.frame.ModelFrame attribute)
learning_curve() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
ledoit_wolf() (pandas_ml.skaccessors.covariance.CovarianceMethods method)
len() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
liblinear (pandas_ml.skaccessors.svm.SVMMethods attribute)
libsvm (pandas_ml.skaccessors.svm.SVMMethods attribute)
libsvm_sparse (pandas_ml.skaccessors.svm.SVMMethods attribute)
linear_model (pandas_ml.core.frame.ModelFrame attribute)
LinearModelMethods (class in pandas_ml.skaccessors.linear_model)
lm (pandas_ml.core.frame.ModelFrame attribute)
locally_linear_embedding() (pandas_ml.skaccessors.manifold.ManifoldMethods method)
log_loss() (pandas_ml.skaccessors.metrics.MetricsMethods method)
log_proba (pandas_ml.core.generic.ModelPredictor attribute)
LRN (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
LRP (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
M
make_pipeline (pandas_ml.skaccessors.pipeline.PipelineMethods attribute)
make_union (pandas_ml.skaccessors.pipeline.PipelineMethods attribute)
manifold (pandas_ml.core.frame.ModelFrame attribute)
ManifoldMethods (class in pandas_ml.skaccessors.manifold)
markedness (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
max() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
MCC (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
mean_shift() (pandas_ml.skaccessors.cluster.ClusterMethods method)
metrics (pandas_ml.core.frame.ModelFrame attribute)
MetricsMethods (class in pandas_ml.skaccessors.metrics)
min() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
mixture (pandas_ml.core.frame.ModelFrame attribute)
model_selection (pandas_ml.core.frame.ModelFrame attribute)
ModelFrame (class in pandas_ml.core.frame)
ModelFrameGroupBy (class in pandas_ml.core.groupby)
ModelPredictor (class in pandas_ml.core.generic)
ModelSelectionMethods (class in pandas_ml.skaccessors.model_selection)
ModelSeries (class in pandas_ml.core.series)
ModelSeriesGroupBy (class in pandas_ml.core.groupby)
ModelTransformer (class in pandas_ml.core.generic)
ms (pandas_ml.core.frame.ModelFrame attribute)
multiclass (pandas_ml.core.frame.ModelFrame attribute)
multioutput (pandas_ml.core.frame.ModelFrame attribute)
N
N (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
naive_bayes (pandas_ml.core.frame.ModelFrame attribute)
neg_class (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
NegativeTest (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
neighbors (pandas_ml.core.frame.ModelFrame attribute)
NeighborsMethods (class in pandas_ml.skaccessors.neighbors)
neural_network (pandas_ml.core.frame.ModelFrame attribute)
NPV (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
O
oas() (pandas_ml.skaccessors.covariance.CovarianceMethods method)
orthogonal_mp_gram() (pandas_ml.skaccessors.linear_model.LinearModelMethods method)
over_sampling (pandas_ml.imbaccessors.base.ImbalanceMethods attribute)
P
P (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
PairGrid() (pandas_ml.snsaccessors.base.SeabornMethods method)
pairwise (pandas_ml.skaccessors.metrics.MetricsMethods attribute)
pandas_ml (module)
pandas_ml.compat (module)
pandas_ml.confusion_matrix (module)
pandas_ml.confusion_matrix.abstract (module)
pandas_ml.confusion_matrix.bcm (module)
pandas_ml.confusion_matrix.cm (module)
pandas_ml.confusion_matrix.stats (module)
pandas_ml.confusion_matrix.test (module)
pandas_ml.core (module)
pandas_ml.core.accessor (module)
pandas_ml.core.base (module)
pandas_ml.core.frame (module)
pandas_ml.core.generic (module)
pandas_ml.core.groupby (module)
pandas_ml.core.series (module)
pandas_ml.imbaccessors (module)
pandas_ml.imbaccessors.base (module)
pandas_ml.imbaccessors.test (module)
pandas_ml.misc (module)
pandas_ml.misc.patsy_wraps (module)
pandas_ml.misc.test (module)
pandas_ml.misc.test.test_patsy (module)
pandas_ml.skaccessors (module)
pandas_ml.skaccessors.base (module)
pandas_ml.skaccessors.cluster (module)
pandas_ml.skaccessors.covariance (module)
pandas_ml.skaccessors.cross_decomposition (module)
pandas_ml.skaccessors.decomposition (module)
pandas_ml.skaccessors.ensemble (module)
pandas_ml.skaccessors.feature_extraction (module)
pandas_ml.skaccessors.feature_selection (module)
pandas_ml.skaccessors.gaussian_process (module)
pandas_ml.skaccessors.isotonic (module)
pandas_ml.skaccessors.linear_model (module)
pandas_ml.skaccessors.manifold (module)
pandas_ml.skaccessors.metrics (module)
pandas_ml.skaccessors.model_selection (module)
pandas_ml.skaccessors.neighbors (module)
pandas_ml.skaccessors.pipeline (module)
pandas_ml.skaccessors.preprocessing (module)
pandas_ml.skaccessors.svm (module)
pandas_ml.skaccessors.test (module)
pandas_ml.skaccessors.test.test_multioutput (module)
pandas_ml.smaccessors (module)
pandas_ml.smaccessors.base (module)
pandas_ml.smaccessors.test (module)
pandas_ml.snsaccessors (module)
pandas_ml.snsaccessors.base (module)
pandas_ml.snsaccessors.test (module)
pandas_ml.test (module)
pandas_ml.tools (module)
pandas_ml.util (module)
pandas_ml.util.testing (module)
pandas_ml.version (module)
pandas_ml.xgboost (module)
pandas_ml.xgboost.base (module)
pandas_ml.xgboost.test (module)
partial_dependence (pandas_ml.skaccessors.ensemble.EnsembleMethods attribute)
partial_dependence() (pandas_ml.skaccessors.ensemble.PartialDependenceMethods method)
PartialDependenceMethods (class in pandas_ml.skaccessors.ensemble)
permutation_test_score() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
pipeline (pandas_ml.core.frame.ModelFrame attribute)
PipelineMethods (class in pandas_ml.skaccessors.pipeline)
plot() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
plot_importance() (pandas_ml.xgboost.base.XGBoostMethods method)
plot_partial_dependence() (pandas_ml.skaccessors.ensemble.PartialDependenceMethods method)
plot_tree() (pandas_ml.xgboost.base.XGBoostMethods method)
PlottingTestCase (class in pandas_ml.util.testing)
population (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
pos_class (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
PositiveTest (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
pp (pandas_ml.core.frame.ModelFrame attribute)
(pandas_ml.core.series.ModelSeries attribute)
PPV (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
precision (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
precision_recall_curve() (pandas_ml.skaccessors.metrics.MetricsMethods method)
precision_recall_fscore_support() (pandas_ml.skaccessors.metrics.MetricsMethods method)
precision_score() (pandas_ml.skaccessors.metrics.MetricsMethods method)
pred (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
PRED_NAME (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
predict() (pandas_ml.core.generic.ModelPredictor method)
(pandas_ml.smaccessors.base.StatsModelsRegressor method)
predict_log_proba() (pandas_ml.core.frame.ModelFrame method)
predict_proba() (pandas_ml.core.frame.ModelFrame method)
predicted (pandas_ml.core.generic.ModelPredictor attribute)
preprocessing (pandas_ml.core.frame.ModelFrame attribute)
(pandas_ml.core.series.ModelSeries attribute)
PreprocessingMethods (class in pandas_ml.skaccessors.preprocessing)
prevalence (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
print_stats() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
proba (pandas_ml.core.generic.ModelPredictor attribute)
prop_test() (in module pandas_ml.confusion_matrix.stats)
Q
qda (pandas_ml.core.frame.ModelFrame attribute)
R
random_projection (pandas_ml.core.frame.ModelFrame attribute)
random_state (pandas_ml.util.testing.TestCase attribute)
recall (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
recall_score() (pandas_ml.skaccessors.metrics.MetricsMethods method)
regression_models (pandas_ml.skaccessors.gaussian_process.GaussianProcessMethods attribute)
RegressionModelsMethods (class in pandas_ml.skaccessors.gaussian_process)
roc_auc_score() (pandas_ml.skaccessors.metrics.MetricsMethods method)
roc_curve() (pandas_ml.skaccessors.metrics.MetricsMethods method)
rugplot() (pandas_ml.snsaccessors.base.SeabornMethods method)
S
sample() (pandas_ml.core.frame.ModelFrame method)
score() (pandas_ml.core.frame.ModelFrame method)
seaborn (pandas_ml.core.frame.ModelFrame attribute)
SeabornMethods (class in pandas_ml.snsaccessors.base)
semi_supervised (pandas_ml.core.frame.ModelFrame attribute)
sensitivity (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
set_params() (pandas_ml.smaccessors.base.StatsModelsRegressor method)
silhouette_samples() (pandas_ml.skaccessors.metrics.MetricsMethods method)
silhouette_score() (pandas_ml.skaccessors.metrics.MetricsMethods method)
sns (pandas_ml.core.frame.ModelFrame attribute)
sparse_encode() (pandas_ml.skaccessors.decomposition.DecompositionMethods method)
SPC (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
specificity (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
spectral_clustering() (pandas_ml.skaccessors.cluster.ClusterMethods method)
spectral_embedding() (pandas_ml.skaccessors.manifold.ManifoldMethods method)
split() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
stats() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
(pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix method)
stats_class (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
stats_overall (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
StatsModelsRegressor (class in pandas_ml.smaccessors.base)
StratifiedShuffleSplit() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
sum() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
support (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
svm (pandas_ml.core.frame.ModelFrame attribute)
SVMMethods (class in pandas_ml.skaccessors.svm)
T
target (pandas_ml.core.frame.ModelFrame attribute)
target_name (pandas_ml.core.frame.ModelFrame attribute)
teardown_method() (pandas_ml.util.testing.PlottingTestCase method)
test_multioutput() (pandas_ml.skaccessors.test.test_multioutput.TestMultiOutput method)
test_objectmapper() (pandas_ml.skaccessors.test.test_multioutput.TestMultiOutput method)
test_patsy_deviation_coding() (pandas_ml.misc.test.test_patsy.TestModelFrame method)
test_patsy_matrices() (pandas_ml.misc.test.test_patsy.TestModelFrame method)
test_patsy_matrix() (pandas_ml.misc.test.test_patsy.TestModelFrame method)
TestCase (class in pandas_ml.util.testing)
TestModelFrame (class in pandas_ml.misc.test.test_patsy)
TestMultiOutput (class in pandas_ml.skaccessors.test.test_multioutput)
text (pandas_ml.skaccessors.feature_extraction.FeatureExtractionMethods attribute)
title (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
TN (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
TNR (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
to_array() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
to_dataframe() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
to_frame() (pandas_ml.core.series.ModelSeries method)
to_graphviz() (pandas_ml.xgboost.base.XGBoostMethods method)
toarray() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
TP (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
TPR (pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix attribute)
train_test_split() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
transform() (pandas_ml.core.frame.ModelFrame method)
(pandas_ml.core.generic.ModelTransformer method)
(pandas_ml.core.groupby.ModelFrameGroupBy method)
(pandas_ml.core.series.ModelSeries method)
transform_with_patsy() (in module pandas_ml.misc.patsy_wraps)
tree (pandas_ml.core.frame.ModelFrame attribute)
true (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
TRUE_NAME (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract attribute)
tsplot() (pandas_ml.snsaccessors.base.SeabornMethods method)
U
under_sampling (pandas_ml.imbaccessors.base.ImbalanceMethods attribute)
V
validation_curve() (pandas_ml.skaccessors.model_selection.ModelSelectionMethods method)
X
xgb (pandas_ml.core.frame.ModelFrame attribute)
XGBClassifier (pandas_ml.xgboost.base.XGBoostMethods attribute)
xgboost (pandas_ml.core.frame.ModelFrame attribute)
XGBoostMethods (class in pandas_ml.xgboost.base)
XGBRegressor (pandas_ml.xgboost.base.XGBoostMethods attribute)
Y
y_pred() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
(pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix method)
y_true() (pandas_ml.confusion_matrix.abstract.ConfusionMatrixAbstract method)
(pandas_ml.confusion_matrix.bcm.BinaryConfusionMatrix method)
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