Version 2.19.240131

Default Value Changes
  • Changed the default value of enable_plotly to True in all visualization module.

  • Removed the matplotlib dependency.

Version 2.19.240124

Bug Fixes
  • Fixed missing fairml metrics in binary_classification_debriefing.

  • Fixed missing parameter issues in fairml.

Version 2.19.240115

Bug Fixes
  • Fixed model debriefing issue.

Version 2.19.240104

Bug Fixes
  • Fixed fairml prediction issue with fixed uuid.

  • Fixed fairml issue when submodel is used.

  • Fixed box_plot issue when plotly is enabled abd groupby is None.

  • Fixed unified regression issue when key is not specified.

  • Fixed massive IsolationForest without key issue.

  • Fixed variable importance issue in model report.

  • Fixed model storage issue when odbc is used.

Version 2.19.231207

New Functions
  • Support STL decomposition method in seasonal_decompose() by offering new parameters decompose_method, stl_robust and stl_seasonal_average.

  • Provide explainability support in LTSF.

  • Offer ignore zero calculation support in UnifiedRegression when calculating MPE or MAPE.

  • Provide top N verbose classes in predict () of UnifiedClassification by offering a new parameter verbose_top_n.

  • Support Stock keeping oriented Prediction Error Costs (SPEC) in AutomaticTimeSeries.

  • Enhanced AutoML logging with Auto SQL Content.

  • Enhanced operation, mutate and filter in DataFrame.

  • Enhanced reset_config_dict by allowing the custom config_dict.

  • Boost dataset report with new PAL describe function.

  • Enhanced proximal gradient support in HGBT including unified classification.

  • Enhanced tf_analysis with enable_stopwords and keep_numeric parameters.

  • Enhanced indicator for model deletion in model storage.

  • Enhanced OneHotEncoding with onehot_min_frequency and onehot_max_categories parameters.

  • Enhanced AutoML with connection optimization.

  • Enhanced model list in model storage by adding partial info display option.

  • Enhanced AutoML progress monitor with evaluating tab.

API Changes
  • Added parameters decompose_method, stl_robust, stl_seasonal_average and smooth_method_non_seasonal in seasonal_decompose().

  • Added parameters show_explainer and reference_dict to provide explainability support in LTSF.

  • Added a parameter ignore_zero to offer ignore zero calculation support in UnifiedRegression when calculating MPE or MAPE.

  • Added a parameter verbose_top_n in predict () of UnifiedClassification to present top N verbose classes.

Version 2.18.231114

Bug Fixes
  • Fixed pipeline score issue.

  • Fixed model table error in automl fit with reason code.

Version 2.18.231103

Bug Fixes
  • Fixed key issue in ARIMA explainer.

  • Fixed hana-ml parameter registration issue.

  • Fixed PCA issue in pipeline fit.

  • Fixed missing attributes issue in time series SHAP.

  • Fixed NULL value issue in time series report.

  • Fixed NULL value issue in SHAP visualizer.

  • Fixed describe function issue with duplicate "unique" column.

  • Fixed key issue in timeseries_box_plot when plotly is enabled.

  • Fixed incompatibility issue with pandas 2.0.

  • Fixed dataframe issue when hint clause is used.

  • Fixed csrf token issue in amdp deployer.

Version 2.18.230927

Bug Fixes
  • Fixed cancellation issue in AutoML progress monitor.

  • Fixed log cleanup issue in AutoML.

  • Fixed usage of concat in Graph.describe().

  • Fixed temporary table issue by replacing with table variable.

  • Fixed missing parameter issue in HGBT regression.

  • Fixed syntax error in louvain.

Version 2.18.230914

New Functions
API Changes
  • Added a parameter called network_type in LTSF for network selection.

  • Enhanced HANA scheduler by removing manual parameters input.

Bug Fixes
  • Fixed scatter plot error from ax.scatter c to cmap.

  • Fixed BAS incompatibility issue.

  • Fixed time diff error when creating new timeframe.

  • Fixed date type issue in dataset report.

  • Fixed time series report issue in changepoints_item.

Version 2.17.230808

Bug Fixes

Version 2.17.230727

Bug Fixes

Version 2.17.230714

Bug Fixes
  • Fixed wrong error message in HANAScheduler.

  • Fixed corr issue that the column misses quotes.

  • Fixed front-end connection reset issue in AutoML to avoid too many query from progress table.

  • Fixed cron missing issue by adding NULL check.

  • Fixed the Decimal issue in TimeSeriesReport.

  • Fixed the x-axis order issue in TimeSeriesReport.

Version 2.17.230628

Bug Fixes
  • Fixed CAP generation issues for APL.

  • Fixed duplicated prefix for predict artifact in CAP generation.

  • Fixed parameter checking for APL.

Version 2.17.230622

New Functions
  • Enhanced the support of plotly for eda functions like quarter_plot(), seasonal_plot() ...

  • Enhanced the support of spectral clustering in UnifiedClustering

  • Enhanced HANA artifacts generation for pipeline module.

  • Enhanced AutoML with reason code option.

  • Enhanced TimeSeriesReport with confidence interval.

  • Enhanced RDT with prediction interval in UnifiedRegression.

  • Enhanced ModelStorage with server-side scheduler.

  • Enhanced unified API for pivoted input data.

  • Enhanced diff() to support datetime column.

Version 2.16.230601

Bug Fixes

Version 2.16.230526

Bug Fixes
  • Fixed pipeline missing evaluation function.

  • Fixed tips and chart width for model report.

  • Fixed built-in operation missing in pipeline module.

  • Fixed WordCloud issues to disable stopwords.

Version 2.16.230519

Bug Fixes
  • Fixed AutomaticTimeSeries config_dict template.

  • Fixed progress logging in auto-ml module.

  • Fixed the progress monitor GeneralProgressStatusMonitor issue when early_stop is enabled.

  • Fixed KNN NaN issue due to the pandas new changes.

  • Fixed describe() function to support SMALLINT.

Version 2.16.230508

Bug Fixes
  • Fixed pipeline module for model storage.

  • Fixed stuck in progress monitor when progress table is not empty.

  • Fixed quote issue in serializing pipeline object.

  • Fixed parameter missing in FACCM.

  • Fixed dependency issue for pydotplus.

  • Fixed tail() function with default rel_col.

  • Fixed NaN in KNN optimal parameter collect.

Version 2.16.230413

Bug Fixes

Version 2.16.230323

Bug Fixes
  • Fixed wrong error message when "hint" has been used.

  • Fixed load_model issue for APL model.

Version 2.16.230316

New Functions
API Changes

Version 2.15.230217

Bug Fixes
  • Fixed cmap issues in eda visualizer.

  • Fixed FFM label bug.

  • Fixed missing word_cloud module issue.

Version 2.15.230111

Bug Fixes
  • Fixed the blank chart issue in dataset report.

  • Fixed dataset report crash due to empty column.

Version 2.15.221223

Bug Fixes
  • Fixed detected season change-points missing error of BCPD.

  • Fixed Change points Chart in TimeSeriesReport.

Version 2.15.221216

New Functions
API Changes
  • Change the "JSON" column to NCLOB table type in ModelStorage.

Version 2.14.221208

Bug Fixes
  • Fixed dependency issues in dataset report.

  • Fixed documentation link in PyPI portal.

  • Fixed replace() function to support NULL replacement.

Version 2.14.221201

Bug Fixes

Version 2.14.221028

Bug Fixes
  • Fixed pipeline monitor when password contains ','.

  • Fixed message not defined error in auto-ml.

  • Fixed pipeline error for PCA, DT and FN when disable_hana_execution() is executed.

  • Fixed json pipeline generation for HGBT and RDT.

  • Fixed parameter name typos for DT and RDT in UnifiedClassification.

  • Fixed execute_statement parser when parameters contain special characters.

Version 2.14.221014

Bug Fixes
  • Fixed legend issues in forecast_line_plot().

  • Fixed duplicated outputs issue in artifact generator.

  • Fixed best pipeline report that points exceed the chart.

  • Fixed progress bar counter issue for AutomaticTimeSeries.

  • Fixed predefined partition in unified API.

Version 2.14.220923

Bug Fixes

Version 2.14.220918

New Functions


API Changes
  • Added initialization parameters handling_missing and json_export in LinearRegression.

  • Added initialization parameters json_export, precompute_lms_sketch, stable_sketch_alg, sparse_sketch_alg in Multi-class LogisticRession.

  • Added parameter periods in seasonal_decompose().

  • Added parameters for APL segmented modeling, segmented forecast and parallel apply: max_tasks and segment_column_name (see APL 2209 and APL 2211 release notes).

Version 2.13.220722

Bug Fixes
  • Fixed early_stop in auto-ml.

  • Fixed display issue in unified report for APL.

Version 2.13.220715

Bug Fixes

Version 2.13.220701

Bug Fixes
  • Fixed table name too long in model storage save_model() function.

  • Fixed mlflow autologging with additional fit parameters.

  • Fixed no mlflow model info display issue.

  • Fixed metric sampling for model report.

  • Fixed wrong schedule template in ModelStorage.

Version 2.13.220608

Bug Fixes
  • fixed identifier length too long issue for function outputs.

Version 2.13.220511

New Functions
API Changes
  • Added parameter interpret in predict() method of KNNClassifier and KNNRegressor for enabling procedure PAL_KNN_INTERPRET.

  • Added parameters sample_size, top_k_attributions, random_state in predict() method of KNNClassifier and KNNRegressor for generating local interpretation result.

  • Enabled missing value handling for input data by adding imputation related parameters in fit(), predict() and score() functions of both UninfiedClassification and UnifiedRegression.

  • Added parameter model_type in GARCH for allowing variant GARCH models.

Bug Fixes
  • Fixed key error bug for parameter param_values in DecisionTreeClassifier and DecisionTreeRegressor.

  • Fixed the encoding error of imputation strategy of NONE type in Imputer.

  • Fixed the key error bug when enabling AFL states for clustering algorithms.

Version 2.12.220428

Bug Fixes
  • Adapted the auto-ml logging according to the PAL function changes.

Version 2.12.220425

Bug Fixes
  • Fixed the display issue for the pipeline report.

  • Fixed the missing ptype issue in AutoML evaluate function.

  • Fixed the transform issue in pipeline fit_predict`() function.

Version 2.12.220408

Bug Fixes

Version 2.12.220325

New Functions
Bug Fixes
  • Fixed m4_sampling() with lowercase column name.

  • Fixed inconsistent IDs assigned to solvers in LogisticRegression() between LOGR and M_LOGR.

  • Fixed a parameter naming error in fft(): flattop_model --> flattop_mode.

  • Fixed a validation error for endog parameter in predict() in Attention.

API Changes
  • Added parameter model_df in the white_noise_test() for selecting the degree of freedom.

  • Added parameter explain_mode in predict() of GRUAttention for selecting the mechanism for generating the reason code for inference results.

Version 2.11.220209

Bug Fixes

Version 2.11.220107

Bug Fixes
  • Fixed box_plot() with lower case column name.

  • Fixed add_id() when the rel_col input is of list type.

  • Fixed shortest_path and shortest_path_one_to_all type cast error.

  • Fixed the alignment error in fast_dtw().

  • Position correction for random search times in LogisticRegression.

  • Fixed HANA hint script generation for resource restriction.

Version 2.11.211211

New Functions
  • Added FeatureSelection.

  • Added BSTS.

  • Added WordCloud.

  • Added hdbprocedure generation in PALBase and applied to all functions.

  • Added GARCH.

  • APL classification, regression, clustering: a new method, 'export_apply_code', generates code which can be used to apply a trained model outside APL.

  • Enhanced Preprocessing with FeatureSelection.

  • Enhanced the ModelStorage with fit parameters in json format.

  • Enhanced PCA categorical support.

  • Enhanced ModelStorage with fit parameters info.

  • Enhanced UnifiedExponentialSmoothing with massive mode.

  • Enhanced UnifiedClassification with AMDP generation as a function.

  • Enhanced ARIMA with an explainer in the predict() function.

  • Enhanced AdditiveModelForecast with an explainer in the predict() function.

  • Enhanced UnifiedClassification with continued training of a trained HybridGradientBoostingClassifier model.

  • Enhanced APL AutoTimeSeries with advanced predict outputs: the 'APL/ApplyExtraMode' parameter can be set in 'extra_applyout_settings'.

  • Enhanced the stored procedure information retrieval.

  • Enhanced fillna() to support non-numeric columns.

  • Enhanced dataset report to convert PAL unsupported type.

API Changes
  • Added initialization parameter background_size, and thread_ratio, top_k_attributions, trend_mod, trend_width, seasonal_width in the predict() method of ARIMA and AutoARIMA.

  • Added parameters show_explainer, decompose_seasonality, decompose_holiday in the predict() function of AdditiveModelForecast.

  • Added warm_start in the fit() method of HybridGradientBoostingClassifier as well as the fit() method of HybridGradientBoostingRegressor for continued training with existing model.

Bug Fixes
  • Fixed index creation bug in on-premise text_classification api.

  • Fixed multi-class LogisticRegression init check bug.

  • Fixed has_table() error for local temporary tables.

Version 2.10.210918

New Functions
  • Added dtw() for generic dynamic time warping with predefined and custom defined step pattern.

  • Added wavedec() for multi-level discrete wavelet transformation, and waverec() for the corresponding inverse transformation.

  • Added wpdec() and wprec() for multi-level (discrete) wavelet packet transformation and inverse.

  • Added OnlineMultiLogisticRegression which is the online version of Multi-Class Logistic Regression.

  • Added SpectralClustering.

  • Added LSTM with attention.

  • Added OneHotEncoding.

  • Added unified preprocessor.

  • Added plot() method for Pipeline.

  • Added UnifiedExponentialSmoothing.

API Changes

Version 2.9.210726

Bug Fixes
  • Fixed load model initialized error in model storage service.

  • Fixed bad link in pypi portal.

Version 2.9.210709

Bug Fixes
  • Fixed missing WeaklyConnectedComponents in hana_ml.graph.algorithms.

  • Fixed missing statistics in hana_ml.graph.Graph.describe.

  • Fixed a bug, where the Graph object creation and discover_graph_workspace() and Graph.describe() do not work on an on-premise system

Version 2.9.210630

Bug Fixes

Version 2.9.210619

  • Constants for directions used in graph functions can be found in hana_ml.graph.constants.DIRECTION_*

  • Following functions and objects are now available in hana_ml.graph for import

    • Graph object

    • create_graph_from_dataframes and create_graph_from_hana_dataframes factory methods

    • discover_graph_workspaces

    • discover_graph_workspace

  • The geometries do not need to be to be specified when creating a DataFrame instance anymore. The geometries are analyzed automatically.

  • Support list of targets and trans_param in feature_tool.

  • Enhanced unified report for UnifiedRegression to view feature importance.

  • Enhanced join() to support list of DataFrame.

  • Enhanced union() to support list of DataFrame.

  • Streamlined the create_dataframe_from_pandas() geo parameters. Now there is only one list of geo_cols, which supports column references as well as (lon, lat) tuples, and one SRID parameter for all columns

  • When you call create_dataframe_from_pandas`() and pass a GeoPandas DataFrame, the geometry column will be detected automatically and processed as a geometry. You don't need to add it manually to geo_cols

  • The Graph constructor is simplified. You can instantiate a graph simply by the workspace name.

  • Enhanced ModelStorage for APL to support HANA Data Lake.

New Functions
  • Introduced hana_ml.graph.algorithms which contains all graph algorithms in the future. The package provides a AlgorithmBase class which can be used to build additional algorithms for a graph.

  • Add hana_ml.graph.algorithms.ShortestPath, which replaces Graph.shortest_path

  • Add hana_ml.graph.algorithms.Neighbors, which replaces Graph.neighbors

  • Add hana_ml.graph.algorithms.NeighborsSubgraph, which replaces Graph.neighbors_with_edges

  • Add hana_ml.graph.algorithms.KShortestPaths

  • Add hana_ml.graph.algorithms.ShortestPathsOneToAll

  • Add hana_ml.graph.discovery.discover_graph_workspace, which reads the metadata of a graph

  • Add hana_ml.graph.create_graph_from_edges_dataframe

  • Add hana_ml.graph.Graph.has_vertices, to check if a list of vertices exist in a graph

  • Add hana_ml.graph.Graph.subgraph, to create a vertices or edges induced subgraph

  • Add hana_ml.graph.Graph.describe, to get some statistics

  • Add hana_ml.graph.Graph.degree_distribution

  • Add hana_ml.DataFrame.srids, which returns the SRS of each geometry column

  • Add hana_ml.DataFrame.geometries, which returns the geometry columns if there are any

  • Add hana_ml.spatial package, that contains

    • create_predefined_srs

    • is_srs_created

    • get_created_srses

  • Add hana_ml.docstore package, that contains

    • create_collection_from_elements

  • Added BCPD for Bayesian change point detection.

  • Added shape() method in DataFrame.

  • Added sort_values(), sort_index() in DataFrame.

  • Added scheduler for model renew in ModelStorage.

  • Added min(), max(), mean(), median(), sum(), value_counts() in DataFrame.

  • Added SHAP support for UnifiedClassification.

  • Added data lake support in model_storage.

  • Added data lake support in dataframe functions.

  • Added line plot for time series forecast.

  • Added split_column() method in DataFrame.

  • Added concat_columns() method in DataFrame.

  • Added outlier_detection_kmeans(), which detects outliers in datasets based on the result of k-means clustering.

  • Added intermittent_forecast() for forecasting intermittent demand data(time-series).

  • Added OnlineLinearRegression which is an online version of the Linear Regression.

API Changes
  • Removed geo_cols from dataframe.create_dataframe_from_shapefile

  • Removed geo_cols from ConnectionContext.sql()

  • Removed geo_cols from ConnectionContext.table()

  • Removed Graph.neighbors and Graph.neighbors_with_edges

  • Removed Graph.shortest_path

  • Removed hana_ml.graph.Path. This is not used anymore

  • Removed hana_ml.graph.create_hana_graph_from_existing_workspace. This is replaced by a simplified Graph object constructor.

  • Renamed hana_ml.graph.create_hana_graph_from_vertex_and_edge_frames to create_graph_from_dataframes

  • Changed the type of geo_cols in create_dataframe_from_pandas to list, which supports direct column references or (lon, lat) tuples for generating POINT geometries

Bug Fixes
  • Fixed inflexible default locations of selected columns of input data, e.g. key, features and endog.

  • Fixed model report's feature importance when it has 0 importance.

Version 2.8.210421

Version 2.8.210421 supports SAP HANA SPS05 and SAP HANA Cloud

Bug Fixes
  • Fixed model report's feature importance when it has 0 importance.

  • Fixed pivot_table() with multiple index issue.

  • Fixed the shap display for categorical columns.

Version 2.8.210321

Version 2.8.210321 supports SAP HANA SPS05 and SAP HANA Cloud

  • Enhanced sql() to enable multiline execution.

  • Enhanced save() to add append option.

  • Enhanced diff() to enable negative input.

  • Enhanced model report functionality of UnifiedClassification with added model and data visualization.

  • Enhanced dataset report module with a optimized process of report generation and better user experience.

  • Enhanced UnifiedClustering() to support parameter distance_level in AgglomerateHierarchicalClustering and DBSCAN functions. Please refer to documentation for details.

  • Enhanced model storage to support unified report.

New Functions
New Dependency
  • Added new dependency 'htmlmin' for generating dataset and model report.

API Changes
  • Added parameters use_fast_library and use_float to KMeans.

  • Added parameter build_report to UnifiedRegression.

  • Added parameter distance_level in UnifiedClustering when func is AgglomerateHierarchicalClustering and DBSCAN. Please refer to documentation for details.

  • Renamed batch_size by chunk_size in create_dataframe_from_pandas().

  • Added initialization parameters random_state and random_initialization for OnlineARIMA, and its partial_fit() method now supports two additional parameters -- learning_rate and epsilon for updating the values in the input model.

Bug Fixes
  • Fixed model storage support issue for OnlineARIMA.

  • Fixed inflexible default locations of selected columns of input data, e.g. key, features and endog.

  • Fixed accuracy_measure issue in AutoExponentialSmoothing.

Version 2.6.210126

Version 2.6.210126 supports SAP HANA SPS05 and SAP HANA Cloud

Bug Fixes

Version 2.6.210113

Version 2.6.210113 supports SAP HANA SPS05 and SAP HANA Cloud

Bug Fixes
  • Fixed load_model issue for KMeans clustering.

  • Removed pypi installation of Shapely for windows user.

  • Fixed duplicate rows bug in save() function.

  • Fixed loading issue in model report.

  • Replaced the option batch_size with chunk_size in create_dataframe_from_pandas().

Version 2.6.201209

Version 2.6.201209 supports SAP HANA SPS05 and SAP HANA Cloud

Bug Fixes
  • Remove shap from installation.

  • Fixed bugs in ConnectionContext when autocommit=False.

  • Fixed font properties bugs in hana_ml.visualizers.eda functions.

  • APL Documentation: other_train_apl_aliases is now documented.

  • APL Gradient Boosting Classification: the target variable won't be displayed in prediction if it is not given in input.

  • APL Gradient Boosting: the default parameter values are now set in the APL backend level. They won't be set in the Python API level.

  • Fixed handling of geometry columns in the context of collect() calls.

  • Fixed shapely not being a required dependency.

  • Fixed the displacement of parameter dispersion in CPD.

Version 2.6.201106

Version 2.6.201116 supports SAP HANA SPS05 and SAP HANA Cloud

New Functions
  • Added kdeplot() for 1D and 2D kde plotting.

  • Added SHAPLEY visualization module shap().

Bug Fixes
  • Fixed incompatibility issue with matplotlib>=3.3.0.

Version 2.6.201016(2.6.200928)

Version 2.6.201016 supports SAP HANA SPS05 and SAP HANA Cloud

API Changes
  • HybridGradientBoostingClassifier and HybridGradientBoostingRegressor: added a parameter adopt_prior to indicate whether to adopt the prior distribution of the target as the initial point.

  • Added parameters compression, max_bits, max_quantization_iter for the following SVM classes:

  • RDTClassifier: added parameters compression, max_bits, quantize_rate for model compression.

  • RDTRegressor: added parameters compression, max_bits, quantize_rate, fittings_quantization for model compression.

  • In predict() method function ARIMA and AutoARIMA, a new value 'truncation_algorithm' of parameter forecast_method is introduced to improve the prediction performance.

  • New intialization parameters string_variable, variable_weight are added to KNNClassifier, KNNRegressor and DBSCAN to enable distance calculation based on String distance.

  • New parameters extrapolation, smooth_width, auxiliary_normalitytest are added to seasonal_decompose() function.

New functions
Bug Fixes
  • Fixed ROC curve display in model report with disordered points.

  • Fixed load_model() for UnifiedClassification in model storage service.

  • Fixed model_selection for UnifiedClassification.

Version 2.5.200626

Version 2.5.200626 supports SAP HANA SPS05 and SAP HANA Cloud

API Changes
New Functions
  • Enhanced smart sampling for visualizers.

  • Enhanced import function to SAP HANA.

  • Enhanced bytes, TIMESTAMP and BIGINT support in create_dataframe_from_pandas().

  • Enhanced TIMESTAMP and DATE support in describe().

  • Predictions made with APL gradient boosting can now be complemented with the reasons that led to these predictions: number of top or bottom explanatory variables, strength values, etc.

  • Supported more data types, SMALLINT, DECIMAL, TINYINT, BIGINT, CLOB and BLOB in dtypes(), generate_table_type() and is_numeric().

  • Enhanced the missing value handling ability in the groupby column by creating a new class for missing values for the following EDAVisualizer functions:

  • Predictions made with APL gradient boosting can now be complemented with the reasons that led to these predictions: number of top or bottom explanatory variables, strength values, etc.

  • APL gradient boosting can provide metrics about feature interactions strength.

  • The connection parameter is no longer required for APL model creation.

Bug Fixes
  • Fixed wrong ID issue in fit function by adding key initialization parameter in ARIMA and AutoARIMA.

  • Fixed CLOB type issue in create_dataframe_from_pandas() by adding parameters table_structure and drop_exit_tab.

  • Fixed pivot_table() index naming bug.

  • Fixed temporary view from temporary table issue in APL time series function by adding sort_data and get_horizon_wide_metric.

  • Fixed bugs in create_dataframe_from_pandas() if the table is temporary.

  • Fixed bugs for data type of init centers in GaussianMixture.

  • Fixed bugs when some data types, e.g. SMALLINT, DECIMAL or TINYINT are not supported in dtypes(), generate_table_type() and is_numeric().

  • Fixed bugs when data types, e.g. DATE and TIMESTAMP, are not supported in describe().

  • Fixed the table overwrite bug in save() if the table name is duplicate.

  • Fixed missing quotation mark in column name bugs in EDA.

  • Users can set 'Cutting Strategy' in APL Gradient Boosting.

  • APL models are saved correctly.

Deprecated Functions
  • GradientBoostingClassifier.

  • GradientBoostingRegressor.

Version 1.0.8

Version 1.0.8 supports SAP HANA SP04 (100% coverage for SAP HANA SPS04 PAL algorithms)

New Functions
  • Added functions in data_manipulation().

  • Added cross-validation options to SAP HANA PAL functions.

  • Added visualizers (EDA profiler).

  • Added model storage services.