zero-one.geni.ml.regression — zero.one/geni 0.0.31

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/latest/api/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.html Timestamp: 2020-10-02T14:21:20.345Z

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/latest/api/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.html Timestamp: 2020-10-02T14:21:20.720Z

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:

$$ \begin{align} y = w_0 + \sum\limits^n_{i-1} w_i x_i + \sum\limits^n_{i=1} \sum\limits^n_{j=i+1} \langle v_i, v_j \rangle x_i x_j \end{align} $$

First two terms denote global bias and linear term (as same as linear regression), and last term denotes pairwise interactions term. v_i describes the i-th variable with k factors.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/latest/api/scala/org/apache/spark/ml/regression/FMRegressor.html Timestamp: 2020-10-02T14:21:21.102Z

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:This implementation is for Stochastic Gradient Boosting, not for TreeBoost.Both algorithms learn tree ensembles by minimizing loss functions.TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not.When the loss is SquaredError, these methods give the same result, but they could differ for other loss functions.We expect to implement TreeBoost in the future: [https://issues.apache.org/jira/browse/SPARK-4240]

Source: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/ml/regression/GBTRegressor.html Timestamp: 2020-10-02T14:21:21.485Z

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."gaussian" : "identity", "log", "inverse""binomial" : "logit", "probit", "cloglog""poisson" : "log", "identity", "sqrt""gamma" : "inverse", "identity", "log""tweedie" : power link function specified through "linkPower". The default link power in the tweedie family is 1 - variancePower.

Source: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.html Timestamp: 2020-10-02T14:21:21.946Z

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."gaussian" : "identity", "log", "inverse""binomial" : "logit", "probit", "cloglog""poisson" : "log", "identity", "sqrt""gamma" : "inverse", "identity", "log""tweedie" : power link function specified through "linkPower". The default link power in the tweedie family is 1 - variancePower.

Source: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.html Timestamp: 2020-10-02T14:21:21.946Z

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."gaussian" : "identity", "log", "inverse""binomial" : "logit", "probit", "cloglog""poisson" : "log", "identity", "sqrt""gamma" : "inverse", "identity", "log""tweedie" : power link function specified through "linkPower". The default link power in the tweedie family is 1 - variancePower.

Source: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.html Timestamp: 2020-10-02T14:21:21.946Z

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/latest/api/scala/org/apache/spark/ml/regression/IsotonicRegression.html Timestamp: 2020-10-02T14:21:22.305Z

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:squaredError (a.k.a squared loss)huber (a hybrid of squared error for relatively small errors and absolute error for relatively large ones, and we estimate the scale parameter from training data)This supports multiple types of regularization:none (a.k.a. ordinary least squares)L2 (ridge regression)L1 (Lasso)L2 + L1 (elastic net)The squared error objective function is: $$ \begin{align} \min_{w}\frac{1}{2n}{\sum_{i=1}^n(X_{i}w - y_{i})^{2} + \lambda\left[\frac{1-\alpha}{2}{||w||{2}}^{2} + \alpha{||w||{1}}\right]} \end{align} $$ The huber objective function is: $$ \begin{align} \min_{w, \sigma}\frac{1}{2n}{\sum_{i=1}^n\left(\sigma + H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \frac{1}{2}\lambda {||w||_2}^2} \end{align} $$ where $$ \begin{align} H_m(z) = \begin{cases} z^2, & \text {if } |z| < \epsilon, \ 2\epsilon|z| - \epsilon^2, & \text{otherwise} \end{cases} \end{align} $$ Note: Fitting with huber loss only supports none and L2 regularization.

Source: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/ml/regression/LinearRegression.html Timestamp: 2020-10-02T14:21:22.713Z

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/latest/api/scala/org/apache/spark/ml/regression/RandomForestRegressor.html Timestamp: 2020-10-02T14:21:23.298Z