Function reference • hana.ml.r

Model, ConnectionContext, DataFrame and Others
`CallPalAutoWithConnection()`	CallPalAutoWithConnection
`ConvertToHANADataFrame()`	ConvertToHANADataFrame
`Table`	Table
`DataManipulation()`	DataManipulation
`ExecuteLogged()`	ExecuteLogged
`GetColumns()`	GetColumns
`QuoteName()`	QuoteName
`sqlQueryMix()`	sqlQueryMix
`score()`	S3 method for score
`hanaml.ConnectionContext()`	ConnectionContext
`hanaml.DataFrame()`	hanaml DataFrame
`hanaml.ModelStorage()`	ModelStorage
`hanaml.train()`	Unified API for model training
`hanaml.trainControl()`	Train Control Parameters for Model Selection
`hanaml.expand.grid()`	Parameter Grid Expansion
`hanaml.range()`	Range values in a named list
`hanaml.sqltrace()`	hanaml.sqltrace
`hanaml.dbplyr2hana()`	dbplyr2hana
`hanaml.hana2dbplyr()`	hana2dbplyr
Auto ML
`hanaml.AutomaticClassification()`	Automatic Classification
`predict(<AutomaticClassification>)`	Make Predictions from an "hanaml.AutomaticClassification" Object
`hanaml.AutomaticRegression()`	Automatic Regression
`predict(<AutomaticRegression>)`	Make Predictions from an "hanaml.AutomaticRegression" Object
`hanaml.AutomaticTimeSeries()`	Automatic TimeSeries
`predict(<AutomaticTimeSeries>)`	Make Predictions from an "hanaml.AutomaticTimeSeries" Object
Clustering
`hanaml.AffinityPropagation()`	Affinity Propagation
`hanaml.AgglomerateHierarchical()`	Agglomerate Hierarchical Clustering
`hanaml.DBSCAN()`	DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
`predict(<DBSCAN>)`	Make Predictions from a "DBSCAN" Object
`hanaml.GaussianMixture()`	Gaussian Mixture Model (GMM)
`predict(<GaussianMixture>)`	Make Predictions from a GaussianMixture Object
`hanaml.GeoDBSCAN()`	Geometry DBSCAN
`hanaml.KMeans()`	KMeans
`predict(<KMeans>)`	Make Predictions from a "KMeans" Object
`hanaml.KMedian()`	K-Medians
`hanaml.KMedoid()`	K-Medoids
`hanaml.LatentDirichletAllocation()`	Latent Dirichlet Allocation
`transform(<LatentDirichletAllocation>)`	Make Inference from a "LatentDirichletAllocation" Object
`hanaml.SOM()`	Self-Organizing Maps
`predict(<SOM>)`	Make Predictions from a "SOM" Object
`hanaml.SlightSilhouette()`	Slight Silhouette
`hanaml.SpectralClustering()`	Spectral Clustering
Classfication
`hanaml.AUC()`	Area Under Curve (AUC)
`hanaml.MulticlassAUC()`	Area Under Curve with Multi-class
`hanaml.confusion.matrix()`	Confusion Matrix
`hanaml.CRF()`	Conditional Random Field
`predict(<CRF>)`	Make Predictions from a "CRF" Object
`hanaml.DecisionTreeClassifier()`	Decision Tree Model for Classficiation
`predict(<DecisionTreeClassifier>)`	Make Predictions from a "DecisionTreeClassifier" Object
`score(<DecisionTreeClassifier>)`	Scoring a "DecisionTreeClassifier" model on a given dataset
`hanaml.DiscriminantAnalysis()`	Linear Discriminant Analysis
`predict(<DiscriminantAnalysis>)`	Make Predictions from a "DiscriminantAnalysis" Object
`score(<DiscriminantAnalysis>)`	Make Predictions from a "DiscriminantAnalysis" Object using labeled dataset and return the accuracy score
`transform(<DiscriminantAnalysis>)`	Make Projections from a "DiscriminantAnalysis" Object
`hanaml.HGBTClassifier()`	Hybrid Gradient Boosting Tree (HGBT) Classifier
`predict(<HGBTClassifier>)`	Make Predictions from a "HGBTClassifier" Object
`score(<HGBTClassifier>)`	Make Predictions from a "HGBTClassifier" Object using labeled dataset and return the accuracy score.
`hanaml.KNNClassifier()`	K-Nearest Neighbor(KNN) Classifier
`predict(<KNNClassifier>)`	Make Predictions from a "KNNClassifier" Object
`score(<KNNClassifier>)`	Make Predictions from a "KNNClassifier" Object using labeled dataset and return the accuracy score.
`hanaml.LogisticRegression()`	Logistic Regression
`predict(<LogisticRegression>)`	Make Predictions from a "LogisticRegression" Object
`score(<LogisticRegression>)`	Make Predictions from a "LogisticRegression" Object using labeled dataset and return the accuracy score
`hanaml.MLPClassifier()`	Multi-Layer Perceptron (MLP) Classifier
`predict(<MLPClassifier>)`	Make Predictions from a "MLPClassifier" Object
`score(<MLPClassifier>)`	Make Predictions from an "MLPClassifier" Object using labeled dataset and return the accuracy score.
`hanaml.NaiveBayes()`	Naive Bayes
`predict(<NaiveBayes>)`	Make Predictions from a "NaiveBayes" Object
`score(<NaiveBayes>)`	Make Predictions from a "NaiveBayes" Object using labeled dataset and return the accuracy score.
`hanaml.OneClassSVM()`	One Class SVM
`predict(<OneClassSVM>)`	Make Predictions from a "OneClassSVM" Object
`hanaml.OnlineMultiLogisticRegression()`	Online Multi Logistic Regression
`predict(<OnlineMultiLogisticRegression>)`	Make Predictions from a "OnlineMultiLogisticRegression" Object
`hanaml.RDTClassifier()`	Random Decision Trees for Classification
`predict(<RDTClassifier>)`	Make Predictions from a "RDTClassifier" Object
`score(<RDTClassifier>)`	Make Predictions from a "RDTClassifier" Object using labeled dataset and return the accuracy score
`hanaml.SVC()`	Support Vector Classification (SVC)
`predict(<SVC>)`	Make Predictions from a "SVC" Object
`score(<SVC>)`	Make Predictions from an "SVC" Object using labeled dataset and return the accuracy score
`hanaml.SVRanking()`	Support Vector Ranking
`predict(<SVRanking>)`	Make Predictions from a "SVRanking" Object
Regression
`hanaml.CoxProportionalHazard()`	Cox Proportional Hazard Model
`predict(<CoxProportionalHazard>)`	Make Predictions from a "CoxProportionalHazard" Object
`hanaml.DecisionTreeRegressor()`	Decision Tree Model for Regression
`predict(<DecisionTreeRegressor>)`	Make Predictions from a "DecisionTreeRegressor" Object
`score(<DecisionTreeRegressor>)`	Scoring a "DecisionTreeRegressor" model on a given dataset
`hanaml.ExponentialRegression()`	Exponential Regression
`predict(<ExponentialRegression>)`	Make Predictions from a "ExponentialRegression" Object
`score(<ExponentialRegression>)`	Make Predictions from a "ExponentialRegression" Object using labeled dataset and return the R2 score
`hanaml.GeometricRegression()`	Bi-variate Geometric Regression
`predict(<GeometricRegression>)`	Make Predictions from a "GeometricRegression" Object
`score(<GeometricRegression>)`	Make Predictions from a "GeometricRegression" Object using labeled dataset and return the R2 score
`hanaml.GLM()`	Generalized Linear Model
`predict(<GLM>)`	Make Predictions from a "GLM" Object
`score(<GLM>)`	Make Predictions from a "GLM" Object using labeled dataset and return the R2 score.
`hanaml.HGBTRegressor()`	Hybrid Gradient Boosting Tree (HGBT) Regressor
`predict(<HGBTRegressor>)`	Make Predictions from an "HGBTRegressor" Object
`score(<HGBTRegressor>)`	Make Predictions from a "HGBTRegressor" Object using labeled dataset and return the R2 score.
`hanaml.KNNRegressor()`	K-Nearest Neighbor(KNN) Regressor
`predict(<KNNRegressor>)`	Make Predictions from a "KNNRegressor" Object
`score(<KNNRegressor>)`	Make Predictions from a "KNNRegressor" Object using labeled dataset and return the accuracy score.
`hanaml.LinearRegression()`	Linear Regression
`predict(<LinearRegression>)`	Make Predictions from a "LinearRegression" Object
`hanaml.LogarithmicRegression()`	Bi-variate Natural Logarithmic Regression
`predict(<LogarithmicRegression>)`	Make Predictions from a "LogarithmicRegression" Object
`score(<LogarithmicRegression>)`	Make Predictions from a "LogarithmicRegression" Object using labeled dataset and return the R2 score
`hanaml.MLPRegressor()`	Multi-Layer Perceptron (MLP) Regressor
`predict(<MLPRegressor>)`	Make Predictions from a "MLPRegressor" Object
`score(<MLPRegressor>)`	Make Predictions from a "MLPRegressor" Object using labeled dataset and return the accuracy score.
`hanaml.OnlineLinearRegression()`	Online Linear Regression
`predict(<OnlineLinearRegression>)`	Make Predictions from a "OnlineLinearRegression" Object
`score(<OnlineLinearRegression>)`	Make Predictions from a "OnlineLinearRegression" Object using labeled dataset and return the R2 score.
`hanaml.PolynomialRegression()`	Polynomial Regression
`predict(<PolynomialRegression>)`	Make Predictions from a "PolynomialRegression" Object
`score(<PolynomialRegression>)`	Make Predictions from a "PolynomialRegression" Object using labeled dataset and return the R2 score
`hanaml.OnlineMultiLogisticRegression()`	Online Multi Logistic Regression
`predict(<OnlineMultiLogisticRegression>)`	Make Predictions from a "OnlineMultiLogisticRegression" Object
`score(<OnlineMultiLogisticRegression>)`	Make Predictions from an "OnlineMultiLogisticRegression" Object using labeled dataset and return the accuracy score
`hanaml.RDTRegressor()`	Random Decision Trees for Regression
`predict(<RDTRegressor>)`	Make Predictions from a "RDTRegressor" Object
`score(<RDTRegressor>)`	Make Predictions from a "RDTRegressor" Object using labeled dataset and return the R2 score
`hanaml.SVR()`	Support Vector Regression (SVR)
`predict(<SVR>)`	Make Predictions from a "SVR" Object
`score(<SVR>)`	Make Predictions from a "SVR" Object using labeled dataset and return the R2 score
Association
`hanaml.Apriori()`	Apriori
`hanaml.AprioriLite()`	Lite Apriori
`hanaml.FPGrowth()`	FP-Growth
`hanaml.KORD()`	K-Optimal Rule Discovery (KORD)
`hanaml.SPM()`	Sequential Pattern Mining (SPM)
Time Series
`hanaml.AccuracyMeasure()`	Accuracy Measure
`hanaml.AdditiveModelForecast()`	Additive Model Forecast
`predict(<AdditiveModelForecast>)`	Make Predictions from a "hanaml.AdditiveModelForecast" Object
`hanaml.ARIMA()`	Autoregressive Integrated Moving Average(ARIMA)
`predict(<ARIMA>)`	Make Predictions from a "hanaml.ARIMA" Object
`hanaml.AutoARIMA()`	Auto Autoregressive Integrated Moving Average
`predict(<AutoARIMA>)`	Make Predictions from a "hanaml.AutoARIMA" Object
`hanaml.AutoExponentialSmoothing()`	Auto Exponential Smoothing
`hanaml.BCPD()`	Bayesian Change-point Detection
`hanaml.BrownExponentialSmoothing()`	Brown Exponential Smoothing
`hanaml.BSTS()`	BSTS
`predict(<BSTS>)`	BSTS Predict
`hanaml.CPD()`	Change-point Detection
`hanaml.Correlation()`	Correlation
`hanaml.Croston()`	Croston
`hanaml.DTW()`	Generic Dynamic Time Warping
`hanaml.DWT()`	Discrete wavelet transform.
`hanaml.IDWT()`	Inverse discrete wavelet transform.
`hanaml.FastDTW()`	Fast Dynamic Time Warping
`hanaml.FFT()`	Fast Fourier Transform
`hanaml.GARCH()`	GARCH
`predict(<GARCH>)`	GARCH Predict
`hanaml.LRSeasonalAdjust()`	Linear Regression with Damped Trend and Seasonal Adjust
`hanaml.HierarchicalForecast()`	Hierarchical Forecast
`hanaml.IntermittentForecast()`	Intermittent Time-Series Forecast
`hanaml.LSTM()`	Long Short-Term Memory (LSTM)
`predict(<LSTM>)`	LSTM Predict
`hanaml.GRUAttention()`	Gated Recurrent Units(GRU) based Encoder-Decoder Model with Attention Mechanism
`predict(<GRUAttention>)`	GRU Attention Predict
`hanaml.SingleExponentialSmoothing()`	Single Exponential Smoothing
`hanaml.DoubleExponentialSmoothing()`	Double Exponential Smoothing
`hanaml.TripleExponentialSmoothing()`	Triple Exponential Smoothing
`hanaml.SeasonalDecompose()`	Seasonality Test
`hanaml.TrendTest()`	Trend Test
`hanaml.WhiteNoiseTest()`	White Noise Test
`hanaml.WPT()`	Discrete wavelet packet transform.
`hanaml.IWPT()`	Inverse discrete wavelet packet transform.
`hanaml.OnlineARIMA()`	Online ARIMA
`predict(<OnlineARIMA>)`	Make Predictions from an "OnlineARIMA" Object
`hanaml.VectorARIMA()`	Vector AutoregRessive Moving Average
`predict(<VectorARIMA>)`	Make Predictions from a "VectorARIMA" Object
`hanaml.CrostonTSB()`	Croston TSB
`hanaml.KsTest()`	Kolmogorov-Smirnov Test
`hanaml.OutlierDetectionTS()`	time series outlier detection algorithm
`hanaml.StationarityTest()`	Stationarity Test
`hanaml.Periodogram()`	Periodogram
`hanaml.ROCKET()`	ROCKET
`transform(<ROCKET>)`	Time Series Transform based on a ROCKET Model
`hanaml.LTSF()`	LTSF
`predict(<LTSF>)`	Make Predictions from a "LTSF" Object
`hanaml.ClassificationTS()`	Time Series Classification
`predict(<ClassificationTS>)`	Time Series Predict based on a "ClassificationTS" Model
Prepocessing
`hanaml.CATPCA()`	Categorical Principal Component Analysis (CATPCA)
`transform(<CATPCA>)`	Data projection from a "CATPCA" Object
`hanaml.Discretize()`	Discretize
`transform(<Discretize>)`	Make Transformation from a "Discretize" Object
`hanaml.IQR()`	Inter-Quartile Range
`hanaml.Imputer()`	Imputer
`transform(<Imputer>)`	Make Transformation from an "Imputer" Object
`hanaml.IsolationForest()`	Isolation Forest
`predict(<IsolationForest>)`	Make Predictions from a "hanaml.IsolationForest" Object
`hanaml.MDS()`	Multi-Dimensional Scaling
`hanaml.Partition()`	Partition
`hanaml.PCA()`	Principal Component Analysis (PCA)
`transform(<PCA>)`	Make Projection from a "PCA" Object
`hanaml.FeatureNormalizer()`	Feature Normalizer
`transform(<FeatureNormalizer>)`	Make Transform from a "FeatureNormalizer" Object
`hanaml.FeatureSelection()`	Feature Selection
`hanaml.Sampling()`	Sampling
`hanaml.VarianceTest()`	Variance Test
`hanaml.SMOTE()`	Synthetic Minority Over-sampling Technique
`hanaml.SMOTETomek()`	SMOTETomek
`hanaml.TomekLinks()`	Tomek's links
`hanaml.mcmc()`	Markov chain Monte Carlo
`hanaml.ImputeTS()`	Time-series Missing Value Handling
`transform(<ImputeTS>)`	Time-series Missing Value Handling using Model
Statistics
`hanaml.OnewayAnova()`	One-way Analysis of variance (ANOVA)
`hanaml.OnewayAnovaRepeated()`	Oneway Repeated Analysis of variance (ANOVA)
`hanaml.ChisqGoF()`	Chi-squared Goodness-of-fit(GoF)
`hanaml.ChisqIndependence()`	Chi-squared test of Independence
`hanaml.CDF()`	Cumulative Distribution Function
`hanaml.ConditionIndex()`	Condition Index
`hanaml.DistributionFit()`	Distribution Fitting
`hanaml.Quantile()`	Distribution Quantile
`hanaml.Entropy()`	Entropy
`hanaml.ftest.equal.var()`	Equal Variance Test
`hanaml.FactorAnalysis()`	Factor Analysis
`hanaml.GrubbsTest()`	Grubbs' Test for Outliers
`hanaml.kaplan.meier.survival.analysis()`	Kaplan-Meier Survival Analysis
`hanaml.CovarianceMatrix()`	Computes covariance matrix
`hanaml.PearsonrMatrix()`	Computes correlation matrix using Pearsonr
`hanaml.median.test.1samp()`	One-Sample Median Test
`hanaml.TTest1Samp()`	One Sample T-Test
`hanaml.TTestInd()`	Independent sample TTest
`hanaml.TTestPaired()`	Paired sample TTest
`hanaml.UnivariateAnalysis()`	Univariate Analysis
`hanaml.Wilcoxon()`	Wilcoxon Signed Rank Test
`hanaml.Multinomial()`	Multinomial Distribution
`hanaml.bernoulli()`	Bernoulli Distribution Sampling
`hanaml.beta()`	Beta Distribution Sampling
`hanaml.binomial()`	binomial Distribution Sampling
`hanaml.Cauchy()`	Cauchy Distribution Sampling
`hanaml.chisquared()`	Chisquared Distribution Sampling
`hanaml.exponential()`	Exponential Distribution Sampling
`hanaml.gumbel()`	Gumbel Distribution Sampling
`hanaml.fisher.f()`	fisher.f Distribution Sampling
`hanaml.gamma()`	Gamma Distribution Sampling
`hanaml.geometric()`	Geometric Distribution Sampling
`hanaml.lognormal()`	Lognormal Distribution Sampling
`hanaml.negative.binomial()`	negative.binomial Distribution Sampling
`hanaml.normal()`	Normal Distribution Sampling
`hanaml.PERT()`	PERT Distribution Sampling
`hanaml.Poisson()`	Poisson Distribution Sampling
`hanaml.student.t()`	Student's t Distribution Sampling
`hanaml.uniform()`	Uniform Distribution Sampling
`hanaml.weibull()`	weibull Distribution Sampling
`hanaml.KDE()`	Kernel Density Estimation
`predict(<KDE>)`	apply Kernel Density Estimation analysis
Social Network Analysis
`hanaml.LinkPredict()`	Link Prediction
`hanaml.PageRank()`	Page Rank
Recommender System
`hanaml.ALS()`	Alternating Least Squares
`predict(<ALS>)`	Make Predictions from an "ALS" Object
`hanaml.FRM()`	Factorized Polynomial Regression Models
`predict(<FRM>)`	Make Predictions from an "FRM" Object
`hanaml.FFMClassifier()`	Field-Aware Factorization Machine for classification
`predict(<FFMClassifier>)`	Make Predictions from an "FFMClassifier" Object
`hanaml.FFMRegressor()`	Field-Aware Factorization Machine for regression
`predict(<FFMRegressor>)`	Make Predictions from an "FFMRegressor" Object
`hanaml.FFMRanker()`	Field-Aware Factorization Machine for Ranking
`predict(<FFMRanker>)`	Make Predictions from an "FFMRanker" Object
Miscellaneous
`hanaml.ABC()`	ABC Analysis
`hanaml.TSNE()`	T-distributed Stochastic Neighbour Embedding
`hanaml.WST()`	Weighted Score Table
Unified Interface
`hanaml.UnifiedClassification()`	Unified Classficiation
`predict(<UnifiedClassification>)`	Make Predictions from an "UnifiedClassification" Object
`score(<UnifiedClassification>)`	Scoring from a "UnifiedClassification" object.
`hanaml.UnifiedRegression()`	Unified Regression
`predict(<UnifiedRegression>)`	Make Predictions from an "UnifiedRegression" Object
`score(<UnifiedRegression>)`	Scoring on labeled dataset from a "UnifiedRegression" object
`hanaml.UnifiedClustering()`	Unified Clustering
`predict(<UnifiedClustering>)`	Make Predictions from a "UnifiedClustering" Object
`hanaml.UnifiedExponentialSmoothing()`	Unified Exponential Smoothing
Visualization
`hanaml.M4Sampling()`	M4 Sampling
Text Mining
`hanaml.Get.Related.Doc()`	Get.Related.Doc
`hanaml.Get.Related.Term()`	Get.Related.Term
`hanaml.Get.Relevant.Doc()`	Get.Relevant.Doc
`hanaml.Get.Relevant.Term()`	Get.Relevant.Term
`hanaml.Get.Suggested.Term()`	Get.Suggested.Term
`hanaml.Text.Classification()`	Text.Classification
`hanaml.Text.Collector()`	Text.Collector
`hanaml.Text.TFIDF()`	Text.TFIDF
`hanaml.TF.Analysis()`	TF.Analysis

Reference

Model, ConnectionContext, DataFrame and Others

Auto ML

Clustering

Classfication

Regression

Association

Time Series

Prepocessing

Statistics

Social Network Analysis

Recommender System

Miscellaneous

Unified Interface

Visualization

Text Mining