Developer Guide

Contents

Training and Prediction

Training and prediction algorithms in
Intel® Data Analytics Acceleration Library
(
Intel® DAAL
) include a range of popular machine learning algorithms. Unlike analysis algorithms, which are intended to characterize the structure of data sets, machine learning algorithms model the data. Modeling operates in two major stages:
  1. Training
    .
    At this stage, the algorithm estimates model parameters based on a training data set.
  2. Prediction or decision making
    .
    At this stage, the algorithm uses the trained model to predict the outcome based on new data.
The following major categories of training and prediction methods are available in
Intel DAAL
:
  • Regression methods.
    These methods predict the values of dependent variables (responses) by observing independent variables.
  • Classification methods.
    These methods identify to which sub-population (class) a given observation belongs.
  • Recommendation methods.
    These methods predict the preference that a user would give to a certain item.
  • Neural networks.
    These information processing systems provide methods to approximate functions of large numbers of arguments and to solve different kinds of machine learning tasks in particular.
Training is typically a lot more computationally complex problem than prediction. Therefore, certain end-to-end analytics usage scenarios require that training and prediction phases are done on distinct devices, the training is done on more powerful devices, while prediction is done on smaller devices. Because smaller devices may have stricter memory footprint requirements,
Intel DAAL
separates Training, Prediction, and respective Model in three different class hierarchies to minimize the footprint.
An alternative to training your model with algorithms implemented in
Intel DAAL
is to build a trained model from pre-calculated model parameters, for example, coefficients
β
for Linear Regression. This enables you to use
Intel DAAL
only to get predictions based on the model parameters computed elsewhere.
The Model Builder class provides an interface for adding all the necessary parameters and building a trained model ready for the prediction stage.
The following schema illustrates the use of Model Builder class:
Model Builder class usage schema

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804