The <code>FHT</code> data set has n = 50 observations and p = 100 predictors. The covariance between predictors Xj and Xj' has the same correlation 0.5. See details in Friedman et al. (2010).

datasets

A generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the LASSO and elastic net (adaptive) penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.

Yi Yang

gcdnet

LASSO and Elastic Net (Adaptive) Penalized Least Squares,
Logistic Regression, HHSVM, Squared Hinge SVM and Expectile
Regression using a Fast GCD Algorithm

FHT function

This data frame contains the following columns:<dl>
 <dt>x</dt>
<dd>a matrix with 100 rows and 5000 columns</dd> <dt>y</dt>
<dd>class labels</dd> <dt>y_reg</dt>
<dd>response variable for regression</dd>
 
</dl>

Format

FHT data introduced in Friedman et al. (2010). — FHT

This data frame contains the following columns:<dl>
 <dt>x</dt>
<dd>a matrix with 100 rows and 5000 columns</dd>

 <dt>y</dt>
<dd>class labels</dd>

 <dt>y_reg</dt>
<dd>response variable for regression</dd>


 
</dl>

FHT: FHT data introduced in Friedman et al. (2010).

Description

Usage

Arguments

Format

References

Examples