zero-one.geni.ml.regression — org.clojars.behrica/geni 0.0.42-CB.2

aft-survival-regression clj

(aft-survival-regression params)

Fit a parametric survival regression model named accelerated failure time (AFT) model (see Accelerated failure time model (Wikipedia)) based on the Weibull distribution of the survival time.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.html

Timestamp: 2020-10-19T01:55:51.453Z

decision-tree-regressor clj

(decision-tree-regressor params)

Decision tree learning algorithm for regression. It supports both continuous and categorical features.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.html

Timestamp: 2020-10-19T01:55:52.001Z

fm-regressor clj

(fm-regressor params)

Factorization Machines learning algorithm for regression. It supports normal gradient descent and AdamW solver.

The implementation is based upon:

S. Rendle. "Factorization machines" 2010.

FM is able to estimate interactions even in problems with huge sparsity (like advertising and recommendation system). FM formula is:

FM regression model uses MSE loss which can be solved by gradient descent method, and regularization terms like L2 are usually added to the loss function to prevent overfitting.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/FMRegressor.html

Timestamp: 2020-10-19T01:55:52.555Z

gbt-regressor clj

(gbt-regressor params)

Gradient-Boosted Trees (GBTs) learning algorithm for regression. It supports both continuous and categorical features.

The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

Notes on Gradient Boosting vs. TreeBoost:

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/GBTRegressor.html

Timestamp: 2020-10-19T01:55:53.108Z

generalised-linear-regression clj

(generalised-linear-regression params)

Fit a Generalized Linear Model (see Generalized linear model (Wikipedia)) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports "gaussian", "binomial", "poisson", "gamma" and "tweedie" as family. Valid link functions for each family is listed below. The first link function of each family is the default one.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.html

Timestamp: 2020-10-19T01:55:53.908Z

generalized-linear-regression clj

(generalized-linear-regression params)

Fit a Generalized Linear Model (see Generalized linear model (Wikipedia)) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports "gaussian", "binomial", "poisson", "gamma" and "tweedie" as family. Valid link functions for each family is listed below. The first link function of each family is the default one.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.html

Timestamp: 2020-10-19T01:55:53.908Z

glm clj

(glm params)

Fit a Generalized Linear Model (see Generalized linear model (Wikipedia)) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports "gaussian", "binomial", "poisson", "gamma" and "tweedie" as family. Valid link functions for each family is listed below. The first link function of each family is the default one.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.html

Timestamp: 2020-10-19T01:55:53.908Z

isotonic-regression clj

(isotonic-regression params)

Isotonic regression.

Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.

Uses org.apache.spark.mllib.regression.IsotonicRegression.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/IsotonicRegression.html

Timestamp: 2020-10-19T01:55:54.264Z

linear-regression clj

(linear-regression params)

Linear regression.

The learning objective is to minimize the specified loss function, with regularization. This supports two kinds of loss:

This supports multiple types of regularization:

The squared error objective function is:

The huber objective function is:

where

Note: Fitting with huber loss only supports none and L2 regularization.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/LinearRegression.html

Timestamp: 2020-10-19T01:55:54.848Z

random-forest-regressor clj

(random-forest-regressor params)

Random Forest learning algorithm for regression. It supports both continuous and categorical features.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/regression/RandomForestRegressor.html

Timestamp: 2020-10-19T01:55:55.394Z