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| <div class="title">Modules</div> </div> |
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| <div class="textblock">Here is a list of all modules:</div><div class="directory"> |
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| <tr id="row_0_" class="even"><td class="entry"><img id="arr_0_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('0_')"/><a class="el" href="group__grp__regml.html" target="_self">Regression Models</a></td><td class="desc"></td></tr> |
| <tr id="row_0_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_2_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_3_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_4_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_5_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_6_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_7_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_8_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_0_9_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_1_"><td class="entry"><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__validation.html" target="_self">Cross Validation</a></td><td class="desc"></td></tr> |
| <tr id="row_2_" class="even"><td class="entry"><img id="arr_2_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('2_')"/><a class="el" href="group__grp__linear__solver.html" target="_self">Linear Systems</a></td><td class="desc"></td></tr> |
| <tr id="row_2_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_2_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_3_"><td class="entry"><img id="arr_3_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('3_')"/><a class="el" href="group__grp__matrix__factorization.html" target="_self">Matrix Factorization</a></td><td class="desc"></td></tr> |
| <tr id="row_3_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_3_1_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><a class="el" href="group__grp__svd.html" target="_self">Singular Value Decomposition</a></td><td class="desc">Performs factorization of dense, sparse, and block matrices </td></tr> |
| <tr id="row_4_" class="even"><td class="entry"><img id="arr_4_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('4_')"/><a class="el" href="group__grp__recursive__partitioning.html" target="_self">Tree Methods</a></td><td class="desc"></td></tr> |
| <tr id="row_4_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__decision__tree.html" target="_self">Decision Tree</a></td><td class="desc">Decision Trees. Decision trees use a tree-based predictive model to predict the value of a target variable based on several input variables </td></tr> |
| <tr id="row_4_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><a class="el" href="group__grp__random__forest.html" target="_self">Random Forest</a></td><td class="desc">Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees </td></tr> |
| <tr id="row_5_"><td class="entry"><img id="arr_5_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('5_')"/><a class="el" href="group__grp__association__rules.html" target="_self">Association Rules</a></td><td class="desc"></td></tr> |
| <tr id="row_5_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_6_"><td class="entry"><img id="arr_6_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('6_')"/><a class="el" href="group__grp__clustering.html" target="_self">Clustering</a></td><td class="desc"></td></tr> |
| <tr id="row_6_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_7_"><td class="entry"><img id="arr_7_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('7_')"/><a class="el" href="group__grp__topic__modelling.html" target="_self">Topic Modelling</a></td><td class="desc"></td></tr> |
| <tr id="row_7_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_8_"><td class="entry"><img id="arr_8_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('8_')"/><a class="el" href="group__grp__text__analysis.html" target="_self">Text Analysis</a></td><td class="desc"></td></tr> |
| <tr id="row_8_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_9_"><td class="entry"><img id="arr_9_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('9_')"/><a class="el" href="group__grp__desc__stats.html" target="_self">Descriptive Statistics</a></td><td class="desc"></td></tr> |
| <tr id="row_9_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_9_1_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><a class="el" href="group__grp__correlation.html" target="_self">Pearson's Correlation</a></td><td class="desc">Generates a cross-correlation matrix for all pairs of numeric columns in a table </td></tr> |
| <tr id="row_10_" class="even"><td class="entry"><img id="arr_10_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('10_')"/><a class="el" href="group__grp__stats.html" target="_self">Inferential Statistics</a></td><td class="desc"></td></tr> |
| <tr id="row_10_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_11_" class="even"><td class="entry"><img id="arr_11_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('11_')"/><a class="el" href="group__grp__support.html" target="_self">Support Modules</a></td><td class="desc"></td></tr> |
| <tr id="row_11_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_11_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_11_2_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_11_3_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__data__prep.html" target="_self">Data Preparation</a></td><td class="desc">Provides utility functions helpful for data preparation before modeling </td></tr> |
| <tr id="row_11_4_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_12_" class="even"><td class="entry"><img id="arr_12_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('12_')"/><a class="el" href="group__grp__pca.html" target="_self">Dimensionality Reduction</a></td><td class="desc"></td></tr> |
| <tr id="row_12_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_12_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_13_"><td class="entry"><img id="arr_13_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('13_')"/><a class="el" href="group__grp__tsa.html" target="_self">Time Series Analysis</a></td><td class="desc"></td></tr> |
| <tr id="row_13_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_14_"><td class="entry"><img id="arr_14_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('14_')"/><a class="el" href="group__grp__early__stage.html" target="_self">Early Stage Development</a></td><td class="desc"></td></tr> |
| <tr id="row_14_0_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_14_1_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__kernmach.html" target="_self">Support Vector Machines</a></td><td class="desc">Generates support vector machines for classification, regression, and novelty detection </td></tr> |
| <tr id="row_14_2_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img id="arr_14_2_" src="ftv2mnode.png" alt="o" width="16" height="22" onclick="toggleFolder('14_2_')"/><a class="el" href="group__grp__sketches.html" target="_self">Cardinality Estimators</a></td><td class="desc">A collection of methods to estimate the number of unique values contained in the data </td></tr> |
| <tr id="row_14_2_0_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_14_2_1_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_14_2_2_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><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_14_3_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_14_4_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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_14_5_" class="even"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__linalg.html" target="_self">Linear Algebra Operations</a></td><td class="desc">Provides utility functions for basic linear algebra operations </td></tr> |
| <tr id="row_14_6_"><td class="entry"><img src="ftv2vertline.png" alt="|" width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><a class="el" href="group__grp__utilities.html" target="_self">DB Administrator Utilities</a></td><td class="desc">Provides a collection of user-defined functions for performing common tasks in the database </td></tr> |
| <tr id="row_15_" class="even"><td class="entry"><img id="arr_15_" src="ftv2mlastnode.png" alt="\" width="16" height="22" onclick="toggleFolder('15_')"/><a class="el" href="group__grp__deprecated.html" target="_self">Deprecated Modules</a></td><td class="desc"></td></tr> |
| <tr id="row_15_0_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__profile.html" target="_self">Profile</a></td><td class="desc">Produces a "profile" of a table or view by running a predefined set of aggregates on each column </td></tr> |
| <tr id="row_15_1_" class="even"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__svdmf.html" target="_self">Matrix Factorization</a></td><td class="desc">Computes low-rank approximation of a sparse matrix </td></tr> |
| <tr id="row_15_2_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__quantile.html" target="_self">Quantile</a></td><td class="desc">Computes a quantile value for a column in a table </td></tr> |
| <tr id="row_15_3_" class="even"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><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> |
| <tr id="row_15_4_"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2node.png" alt="o" width="16" height="22" /><a class="el" href="group__grp__dectree.html" target="_self">Decision Tree (old C4.5 implementation)</a></td><td class="desc">Generates a decision tree using the C4.5 algorithm </td></tr> |
| <tr id="row_15_5_" class="even"><td class="entry"><img src="ftv2blank.png" alt=" " width="16" height="22" /><img src="ftv2lastnode.png" alt="\" width="16" height="22" /><a class="el" href="group__grp__rf.html" target="_self">Random Forest (old implementation)</a></td><td class="desc">Constructs a classification model that outputs the class most frequently chosen by many decision trees constructed from a training dataset </td></tr> |
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