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| <span id="projectnumber">1.18.0</span> |
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| <div class="textblock">Here is a list of all modules:</div><div class="directory"> |
| <div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span>]</div><table class="directory"> |
| <tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><a class="el" href="group__grp__datatrans.html" target="_self">Data Types and Transformations</a></td><td class="desc"></td></tr> |
| <tr id="row_0_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_0_0_" class="arrow" onclick="toggleFolder('0_0_')">▼</span><a class="el" href="group__grp__arraysmatrix.html" target="_self">Arrays and Matrices</a></td><td class="desc">Mathematical operations for arrays and matrices </td></tr> |
| <tr id="row_0_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__array.html" target="_self">Array Operations</a></td><td class="desc">Provides fast array operations supporting other MADlib modules </td></tr> |
| <tr id="row_0_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__matrix.html" target="_self">Matrix Operations</a></td><td class="desc">Provides fast matrix operations supporting other MADlib modules </td></tr> |
| <tr id="row_0_0_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_0_0_2_" class="arrow" onclick="toggleFolder('0_0_2_')">►</span><a class="el" href="group__grp__matrix__factorization.html" target="_self">Matrix Factorization</a></td><td class="desc">Linear algebra methods that factorize a matrix into a product of matrices </td></tr> |
| <tr id="row_0_0_2_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__lmf.html" target="_self">Low-Rank Matrix Factorization</a></td><td class="desc">Performs low-rank matrix factorization for an incomplete matrix </td></tr> |
| <tr id="row_0_0_2_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__svd.html" target="_self">Singular Value Decomposition</a></td><td class="desc">Performs factorization of dense and sparse matrices </td></tr> |
| <tr id="row_0_0_3_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__linalg.html" target="_self">Norms and Distance Functions</a></td><td class="desc">Provides utility functions for basic linear algebra operations </td></tr> |
| <tr id="row_0_0_4_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__svec.html" target="_self">Sparse Vectors</a></td><td class="desc">Implements a sparse vector data type that provides compressed storage of vectors that may have many duplicate elements </td></tr> |
| <tr id="row_0_1_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__encode__categorical.html" target="_self">Encoding Categorical Variables</a></td><td class="desc">Functions to encode categorical variables to prepare data for input into predictive algorithms </td></tr> |
| <tr id="row_0_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__path.html" target="_self">Path</a></td><td class="desc">A function to perform complex pattern matching across rows and extract useful information about the matches </td></tr> |
| <tr id="row_0_3_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pivot.html" target="_self">Pivot</a></td><td class="desc">Pivoting and data summarization tools for preparing data for modeling operations </td></tr> |
| <tr id="row_0_4_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__sessionize.html" target="_self">Sessionize</a></td><td class="desc">Session reconstruction of data consisting of a time stampled sequence of events </td></tr> |
| <tr id="row_0_5_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__stemmer.html" target="_self">Stemming</a></td><td class="desc">Provides porter stemmer operations supporting other MADlib modules </td></tr> |
| <tr id="row_1_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">▼</span><a class="el" href="group__grp__dl.html" target="_self">Deep Learning</a></td><td class="desc">A collection of modules for deep learning </td></tr> |
| <tr id="row_1_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_0_" class="arrow" onclick="toggleFolder('1_0_')">▼</span><a class="el" href="group__grp__model__prep.html" target="_self">Model Preparation</a></td><td class="desc">Prepare models and data for deep learning </td></tr> |
| <tr id="row_1_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__input__preprocessor__dl.html" target="_self">Preprocess Data</a></td><td class="desc">Prepare training data for use by deep learning modules </td></tr> |
| <tr id="row_1_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__keras__model__arch.html" target="_self">Define Model Architectures</a></td><td class="desc">Function to load model architectures and weights into a table </td></tr> |
| <tr id="row_1_0_2_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__custom__function.html" target="_self">Define Custom Functions</a></td><td class="desc">Function to load serialized Python objects into a table </td></tr> |
| <tr id="row_1_1_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__keras.html" target="_self">Train Single Model</a></td><td class="desc">Fit, evaluate and predict for one model </td></tr> |
| <tr id="row_1_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_2_" class="arrow" onclick="toggleFolder('1_2_')">▼</span><a class="el" href="group__grp__model__selection.html" target="_self">Train Multiple Models</a></td><td class="desc">Train multiple deep learning models at the same time for model architecture search and hyperparameter selection </td></tr> |
| <tr id="row_1_2_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__keras__setup__model__selection.html" target="_self">Define Model Configurations</a></td><td class="desc">Generate configurations for model architecture search and hyperparameter tuning </td></tr> |
| <tr id="row_1_2_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__keras__run__model__selection.html" target="_self">Train Model Configurations</a></td><td class="desc">Explore network architectures and hyperparameters by training many models a time </td></tr> |
| <tr id="row_1_2_2_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__automl.html" target="_self">AutoML</a></td><td class="desc">Functions to run automated machine learning (autoML) methods for model architecture search and hyperparameter tuning </td></tr> |
| <tr id="row_1_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_1_3_" class="arrow" onclick="toggleFolder('1_3_')">▼</span><a class="el" href="group__grp__dl__utilities.html" target="_self">Utilities for Deep Learning</a></td><td class="desc">Utilities specific to deep learning workflows </td></tr> |
| <tr id="row_1_3_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__gpu__configuration.html" target="_self">Show GPU Configuration</a></td><td class="desc">Utility function to report number and type of GPUs in the database cluster </td></tr> |
| <tr id="row_2_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_2_" class="arrow" onclick="toggleFolder('2_')">▼</span><a class="el" href="group__grp__graph.html" target="_self">Graph</a></td><td class="desc">Graph algorithms and measures associated with graphs </td></tr> |
| <tr id="row_2_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__apsp.html" target="_self">All Pairs Shortest Path</a></td><td class="desc">Finds the shortest paths between every vertex pair in a given graph </td></tr> |
| <tr id="row_2_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__bfs.html" target="_self">Breadth-First Search</a></td><td class="desc">Finds the nodes reachable from a given source vertex using a breadth-first approach </td></tr> |
| <tr id="row_2_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__hits.html" target="_self">HITS</a></td><td class="desc">Find the HITS scores (authority and hub) of all vertices in a directed graph </td></tr> |
| <tr id="row_2_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_2_3_" class="arrow" onclick="toggleFolder('2_3_')">▼</span><a class="el" href="group__grp__graph__measures.html" target="_self">Measures</a></td><td class="desc">A collection of metrics computed on a graph </td></tr> |
| <tr id="row_2_3_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__graph__avg__path__length.html" target="_self">Average Path Length</a></td><td class="desc">Computes the average shortest-path length of a graph </td></tr> |
| <tr id="row_2_3_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__graph__closeness.html" target="_self">Closeness</a></td><td class="desc">Computes the closeness centrality value of each node in the graph </td></tr> |
| <tr id="row_2_3_2_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__graph__diameter.html" target="_self">Graph Diameter</a></td><td class="desc">Computes the diameter of a graph </td></tr> |
| <tr id="row_2_3_3_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__graph__vertex__degrees.html" target="_self">In-Out Degree</a></td><td class="desc">Computes the degrees for each vertex </td></tr> |
| <tr id="row_2_4_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pagerank.html" target="_self">PageRank</a></td><td class="desc">Find the PageRank of all vertices in a directed graph </td></tr> |
| <tr id="row_2_5_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__sssp.html" target="_self">Single Source Shortest Path</a></td><td class="desc">Finds the shortest path from a single source vertex to every other vertex in a given graph </td></tr> |
| <tr id="row_2_6_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__wcc.html" target="_self">Weakly Connected Components</a></td><td class="desc">Find all weakly connected components of a graph </td></tr> |
| <tr id="row_3_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_3_" class="arrow" onclick="toggleFolder('3_')">▼</span><a class="el" href="group__grp__mdl.html" target="_self">Model Selection</a></td><td class="desc">Functions for model selection and model evaluation </td></tr> |
| <tr id="row_3_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__validation.html" target="_self">Cross Validation</a></td><td class="desc">Estimates the fit of a predictive model given a data set and specifications for the training, prediction, and error estimation functions </td></tr> |
| <tr id="row_3_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pred.html" target="_self">Prediction Metrics</a></td><td class="desc">Provides various prediction accuracy metrics </td></tr> |
| <tr id="row_3_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__train__test__split.html" target="_self">Train-Test Split</a></td><td class="desc">A method for splitting a data set into separate training and testing sets </td></tr> |
| <tr id="row_4_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_4_" class="arrow" onclick="toggleFolder('4_')">▼</span><a class="el" href="group__grp__sampling.html" target="_self">Sampling</a></td><td class="desc">A collection of methods for sampling from a population </td></tr> |
| <tr id="row_4_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__balance__sampling.html" target="_self">Balanced Sampling</a></td><td class="desc">A method to independently sample classes to produce a balanced data set. This is commonly used when classes are imbalanced, to ensure that subclasses are adequately represented in the sample </td></tr> |
| <tr id="row_4_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__strs.html" target="_self">Stratified Sampling</a></td><td class="desc">A method for independently sampling subpopulations (strata) </td></tr> |
| <tr id="row_5_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_5_" class="arrow" onclick="toggleFolder('5_')">▼</span><a class="el" href="group__grp__stats.html" target="_self">Statistics</a></td><td class="desc">A collection of probability and statistics modules </td></tr> |
| <tr id="row_5_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_5_0_" class="arrow" onclick="toggleFolder('5_0_')">▼</span><a class="el" href="group__grp__desc__stats.html" target="_self">Descriptive Statistics</a></td><td class="desc">Methods to compute descriptive statistics of a dataset </td></tr> |
| <tr id="row_5_0_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_5_0_0_" class="arrow" onclick="toggleFolder('5_0_0_')">►</span><a class="el" href="group__grp__sketches.html" target="_self">Cardinality Estimators</a></td><td class="desc">Methods to estimate the number of unique values contained in data </td></tr> |
| <tr id="row_5_0_0_0_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__countmin.html" target="_self">CountMin (Cormode-Muthukrishnan)</a></td><td class="desc">Implements Cormode-Mathukrishnan <em>CountMin</em> sketches on integer values as a user-defined aggregate </td></tr> |
| <tr id="row_5_0_0_1_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__fmsketch.html" target="_self">FM (Flajolet-Martin)</a></td><td class="desc">Implements Flajolet-Martin's distinct count estimation as a user-defined aggregate </td></tr> |
| <tr id="row_5_0_0_2_" class="even" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__mfvsketch.html" target="_self">MFV (Most Frequent Values)</a></td><td class="desc">Implements the most frequent values variant of the CountMin sketch as a user-defined aggregate </td></tr> |
| <tr id="row_5_0_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__correlation.html" target="_self">Covariance and Correlation</a></td><td class="desc">Generates a covariance or Pearson correlation matrix for pairs of numeric columns in a table </td></tr> |
| <tr id="row_5_0_2_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__summary.html" target="_self">Summary</a></td><td class="desc">Calculates general descriptive statistics for any data table </td></tr> |
| <tr id="row_5_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_5_1_" class="arrow" onclick="toggleFolder('5_1_')">▼</span><a class="el" href="group__grp__inf__stats.html" target="_self">Inferential Statistics</a></td><td class="desc">Methods to compute inferential statistics of a dataset </td></tr> |
| <tr id="row_5_1_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__stats__tests.html" target="_self">Hypothesis Tests</a></td><td class="desc">Provides functions to perform statistical hypothesis tests </td></tr> |
| <tr id="row_5_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__prob.html" target="_self">Probability Functions</a></td><td class="desc">Provides cumulative distribution, density/mass, and quantile functions for a wide range of probability distributions </td></tr> |
| <tr id="row_6_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_6_" class="arrow" onclick="toggleFolder('6_')">▼</span><a class="el" href="group__grp__super.html" target="_self">Supervised Learning</a></td><td class="desc">Methods to perform a variety of supervised learning tasks </td></tr> |
| <tr id="row_6_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__crf.html" target="_self">Conditional Random Field</a></td><td class="desc">Constructs a Conditional Random Fields (CRF) model for labeling sequential data </td></tr> |
| <tr id="row_6_1_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__knn.html" target="_self">k-Nearest Neighbors</a></td><td class="desc">Finds \(k\) nearest data points to the given data point and outputs majority vote value of output classes for classification, or average value of target values for regression </td></tr> |
| <tr id="row_6_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__nn.html" target="_self">Neural Network</a></td><td class="desc">Solves classification and regression problems with several fully connected layers and non-linear activation functions </td></tr> |
| <tr id="row_6_3_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_3_" class="arrow" onclick="toggleFolder('6_3_')">▼</span><a class="el" href="group__grp__regml.html" target="_self">Regression Models</a></td><td class="desc">A collection of methods for modeling conditional expectation of a response variable </td></tr> |
| <tr id="row_6_3_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__clustered__errors.html" target="_self">Clustered Variance</a></td><td class="desc">Calculates clustered variance for linear, logistic, and multinomial logistic regression models, and Cox proportional hazards models </td></tr> |
| <tr id="row_6_3_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__cox__prop__hazards.html" target="_self">Cox-Proportional Hazards Regression</a></td><td class="desc">Models the relationship between one or more independent predictor variables and the amount of time before an event occurs </td></tr> |
| <tr id="row_6_3_2_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__elasticnet.html" target="_self">Elastic Net Regularization</a></td><td class="desc">Generates a regularized regression model for variable selection in linear and logistic regression problems, combining the L1 and L2 penalties of the lasso and ridge methods </td></tr> |
| <tr id="row_6_3_3_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__glm.html" target="_self">Generalized Linear Models</a></td><td class="desc">Estimate generalized linear model (GLM). GLM is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value </td></tr> |
| <tr id="row_6_3_4_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__linreg.html" target="_self">Linear Regression</a></td><td class="desc">Also called Ordinary Least Squares Regression, models linear relationship between a dependent variable and one or more independent variables </td></tr> |
| <tr id="row_6_3_5_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__logreg.html" target="_self">Logistic Regression</a></td><td class="desc">Models the relationship between one or more predictor variables and a binary categorical dependent variable by predicting the probability of the dependent variable using a logistic function </td></tr> |
| <tr id="row_6_3_6_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__marginal.html" target="_self">Marginal Effects</a></td><td class="desc">Calculates marginal effects for the coefficients in regression problems </td></tr> |
| <tr id="row_6_3_7_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__multinom.html" target="_self">Multinomial Regression</a></td><td class="desc">Multinomial regression is to model the conditional distribution of the multinomial response variable using a linear combination of predictors </td></tr> |
| <tr id="row_6_3_8_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__ordinal.html" target="_self">Ordinal Regression</a></td><td class="desc">Regression to model data with ordinal response variable </td></tr> |
| <tr id="row_6_3_9_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__robust.html" target="_self">Robust Variance</a></td><td class="desc">Calculates Huber-White variance estimates for linear, logistic, and multinomial regression models, and for Cox proportional hazards models </td></tr> |
| <tr id="row_6_4_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__svm.html" target="_self">Support Vector Machines</a></td><td class="desc">Solves classification and regression problems by separating data with a hyperplane or other nonlinear decision boundary </td></tr> |
| <tr id="row_6_5_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_5_" class="arrow" onclick="toggleFolder('6_5_')">▼</span><a class="el" href="group__grp__tree.html" target="_self">Tree Methods</a></td><td class="desc">A collection of recursive partitioning (tree) methods </td></tr> |
| <tr id="row_6_5_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__decision__tree.html" target="_self">Decision Tree</a></td><td class="desc">Decision trees are tree-based supervised learning methods that can be used for classification and regression </td></tr> |
| <tr id="row_6_5_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__random__forest.html" target="_self">Random Forest</a></td><td class="desc">Random forest is an ensemble learning method for classification and regression that construct a multitude of decision trees at training time, then produces the class that is the mean (regression) or mode (classification) of the prediction produced by the individual trees </td></tr> |
| <tr id="row_7_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_7_" class="arrow" onclick="toggleFolder('7_')">▼</span><a class="el" href="group__grp__tsa.html" target="_self">Time Series Analysis</a></td><td class="desc">A collection of methods to analyze time series data </td></tr> |
| <tr id="row_7_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__arima.html" target="_self">ARIMA</a></td><td class="desc">Generates a model with autoregressive, moving average, and integrated components for a time series dataset </td></tr> |
| <tr id="row_8_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_8_" class="arrow" onclick="toggleFolder('8_')">▼</span><a class="el" href="group__grp__unsupervised.html" target="_self">Unsupervised Learning</a></td><td class="desc">A collection of methods for unsupervised learning tasks </td></tr> |
| <tr id="row_8_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_8_0_" class="arrow" onclick="toggleFolder('8_0_')">▼</span><a class="el" href="group__grp__association__rules.html" target="_self">Association Rules</a></td><td class="desc">Methods used to discover patterns in transactional datasets </td></tr> |
| <tr id="row_8_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__assoc__rules.html" target="_self">Apriori Algorithm</a></td><td class="desc">Computes association rules for a given set of data </td></tr> |
| <tr id="row_8_1_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_8_1_" class="arrow" onclick="toggleFolder('8_1_')">▼</span><a class="el" href="group__grp__clustering.html" target="_self">Clustering</a></td><td class="desc">Methods for clustering data </td></tr> |
| <tr id="row_8_1_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__kmeans.html" target="_self">k-Means Clustering</a></td><td class="desc">Partitions a set of observations into clusters by finding centroids that minimize the sum of observations' distances from their closest centroid </td></tr> |
| <tr id="row_8_2_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_8_2_" class="arrow" onclick="toggleFolder('8_2_')">▼</span><a class="el" href="group__grp__pca.html" target="_self">Dimensionality Reduction</a></td><td class="desc">Methods for reducing the number of variables in a dataset to obtain a set of principle variables </td></tr> |
| <tr id="row_8_2_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__pca__train.html" target="_self">Principal Component Analysis</a></td><td class="desc">Produces a model that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components </td></tr> |
| <tr id="row_8_2_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__pca__project.html" target="_self">Principal Component Projection</a></td><td class="desc">Projects a higher dimensional data point to a lower dimensional subspace spanned by principal components learned through the PCA training procedure </td></tr> |
| <tr id="row_8_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_8_3_" class="arrow" onclick="toggleFolder('8_3_')">▼</span><a class="el" href="group__grp__topic__modelling.html" target="_self">Topic Modelling</a></td><td class="desc">A collection of methods to uncover abstract topics in a document corpus </td></tr> |
| <tr id="row_8_3_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__lda.html" target="_self">Latent Dirichlet Allocation</a></td><td class="desc">Generates a Latent Dirichlet Allocation predictive model for a collection of documents </td></tr> |
| <tr id="row_9_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_9_" class="arrow" onclick="toggleFolder('9_')">▼</span><a class="el" href="group__grp__other__functions.html" target="_self">Utilities</a></td><td class="desc"></td></tr> |
| <tr id="row_9_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__cols2vec.html" target="_self">Columns to Vector</a></td><td class="desc">Create a new table with all feature columns inserted into a single column as an array </td></tr> |
| <tr id="row_9_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__utilities.html" target="_self">Database Functions</a></td><td class="desc">Provides a collection of user-defined functions for performing common tasks in the database </td></tr> |
| <tr id="row_9_2_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_9_2_" class="arrow" onclick="toggleFolder('9_2_')">▼</span><a class="el" href="group__grp__linear__solver.html" target="_self">Linear Solvers</a></td><td class="desc">Methods that implement solutions for systems of consistent linear equations </td></tr> |
| <tr id="row_9_2_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__dense__linear__solver.html" target="_self">Dense Linear Systems</a></td><td class="desc">Implements solution methods for large dense linear systems. Currently, restricted to problems that fit in memory </td></tr> |
| <tr id="row_9_2_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__sparse__linear__solver.html" target="_self">Sparse Linear Systems</a></td><td class="desc">Implements solution methods for linear systems with sparse matrix input. Currently, restricted to problems that fit in memory </td></tr> |
| <tr id="row_9_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__minibatch__preprocessing.html" target="_self">Mini-Batch Preprocessor</a></td><td class="desc">Utility that prepares input data for use by models that support mini-batch as an optimization option </td></tr> |
| <tr id="row_9_4_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pmml.html" target="_self">PMML Export</a></td><td class="desc">Implements the PMML XML standard to describe and exchange models produced by data mining and machine learning algorithms </td></tr> |
| <tr id="row_9_5_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__text__utilities.html" target="_self">Term Frequency</a></td><td class="desc">Provides a collection of functions for performing common tasks related to text analytics </td></tr> |
| <tr id="row_9_6_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__vec2cols.html" target="_self">Vector to Columns</a></td><td class="desc">Converts a feature array in a single column of an output table into multiple columns </td></tr> |
| <tr id="row_10_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_10_" class="arrow" onclick="toggleFolder('10_')">▼</span><a class="el" href="group__grp__early__stage.html" target="_self">Early Stage Development</a></td><td class="desc"></td></tr> |
| <tr id="row_10_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__cg.html" target="_self">Conjugate Gradient</a></td><td class="desc">Finds the solution to the function \( \boldsymbol Ax = \boldsymbol b \), where \(A\) is a symmetric, positive-definite matrix and \(x\) and \( \boldsymbol b \) are vectors </td></tr> |
| <tr id="row_10_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__dbscan.html" target="_self">DBSCAN</a></td><td class="desc">Partitions a set of observations into clusters of arbitrary shape based on the density of nearby neighbors </td></tr> |
| <tr id="row_10_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__bayes.html" target="_self">Naive Bayes Classification</a></td><td class="desc">Constructs a classification model from a dataset where each attribute independently contributes to the probability that a data point belongs to a category </td></tr> |
| <tr id="row_10_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__sample.html" target="_self">Random Sampling</a></td><td class="desc">Provides utility functions for sampling operations </td></tr> |
| <tr id="row_11_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_11_" class="arrow" onclick="toggleFolder('11_')">▼</span><a class="el" href="group__grp__deprecated.html" target="_self">Deprecated Modules</a></td><td class="desc"></td></tr> |
| <tr id="row_11_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__indicator.html" target="_self">Create Indicator Variables</a></td><td class="desc">Provides utility functions helpful for data preparation before modeling </td></tr> |
| <tr id="row_11_1_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__mlogreg.html" target="_self">Multinomial Logistic Regression</a></td><td class="desc">Also called as softmax regression, models the relationship between one or more independent variables and a categorical dependent variable </td></tr> |
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