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

Implements a generalized coordinate descent (GCD) algorithm
for computing the solution paths of the hybrid Huberized support vector
machine (HHSVM) and its generalizations. Supported models include the
(adaptive) LASSO and elastic net penalized least squares, logistic
regression, HHSVM, squared hinge loss SVM and expectile regression.

Yi Yang

gcdnet

The (Adaptive) LASSO and Elastic Net Penalized Least Squares,
Logistic Regression, Hybrid Huberized Support Vector Machines,
Squared Hinge Loss Support Vector Machines and Expectile
Regression using a Fast Generalized Coordinate Descent
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

Arguments

Format

References

Examples