Details

Given n feature vectors of n p-dimensional feature vectors a vector of class labels , where and

K

is the number of classes, describes the class to which the feature vector belongs, the problem is to train a logistic regression model.

The logistic regression model is the set of vectors that gives the posterior probability

for a given feature vector and class label for each . See [Hastie2009].

If the categorical variable is binary, the model is defined as a single vector that determines the posterior probability

where the first term is the negative log-likelihood of conditional

Y

given

X

, and the latter terms are regularization ones that penalize the complexity of the model (large values), and are non-negative regularization parameters applied to L1 and L2 norm of vectors in .

Given logistic regression model and vectors , the problem is to calculate the responses for those vectors, and their probabilities and logarithms of probabilities if required. The computation is based on formula (1) in multinomial case and on formula (2) in binary case.

Usage of Training Alternative

Create a Logistic Regression model builder using a constructor with the required number of responses and features.
Use the
setBeta
method to add the set of pre-calculated coefficients to the model. Specify random access iterators to the first and the last element of the set of coefficients [ISO/IEC 14882:2011 §24.2.7]_.
If your set of coefficients does not contain an intercept, interceptFlag is automatically set to
False
, and to
True
, otherwise.
Use the
getModel
method to get the trained Logistic Regression model.
Use the
getStatus
method to check the status of the model building process. If
DAAL_NOTHROW_EXCEPTIONS
macros is defined, the status report contains the list of errors that describe the problems API encountered (in case of API runtime failure).

If after calling the

getModel

method you use the

setBeta

method to update coefficients, the initial model will be automatically updated with the new coefficients.

log_reg_model_builder.cpp

LogRegModelBuilder.java

log_reg_model_builder.py

Batch Processing

Parameter	Default Value	Description
algorithmFPType	float	The floating-point type that the algorithm uses for intermediate computations. Can be float or double .
method	defaultDense	The computation method used by the logistic regression. The only training method supported so far is the default dense method.
nClasses	Not applicable	The number of classes. A required parameter.
interceptFlag	True	A flag that indicates a need to compute
penaltyL1	0	L1 regularization coefficient L1 regularization is not supported on GPU.
penaltyL2	0	L2 regularization coefficient
optimizationSolver	SGD solver	All iterative solvers are available as optimization procedures to use at the training stage: SGD (Stochastic Gradient Descent Algorithm) ADAGRAD (Adaptive Subgradient Method) LBFGS (Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm) SAGA (Stochastic Average Gradient Accelerated Method)

Result ID	Result
probabilities	A numeric table of size containing probabilities of classes computed when computeClassesProbabilities option is enabled.
logProbabilities	A numeric table of size containing logarithms of classes’ probabilities computed when computeClassesLogProbabilities option is enabled.

Examples

log_reg_dense_batch.cpp
log_reg_binary_dense_batch.cpp

LogRegDenseBatch.java
LogRegBinaryDenseBatch.java

log_reg_dense_batch.py
log_reg_binary_dense_batch.py

log_reg_dense_batch.py
log_reg_binary_dense_batch.py