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High-Performance Data Science
This library helps reduce the time it
takes to develop high-performance data science applications. Enable
applications to make better predictions faster and analyze larger data
sets with available compute resources.
Includes highly optimized machine learning and analytics functions
Simultaneously ingests data and computes results for highest throughput performance
Supports batch, streaming, and distribution use models to meet a range of application needs
Use the same API for application development on multiple operating systems
Note This library
supports Python*. To access a Python interface for the Intel® Data
Analytics Acceleration Library (Intel® DAAL) high-speed algorithms, use
the daal4py that is included in the Intel® Distribution for Python*.
Probabilistic classification and variable importance computation for Gradient Boosted Trees.
Classification Stump with Information gain and Gini index split methods.
Regression Stump with MSE split method.
Extended existing functionality:
Decision Tree functionality supports weighted data.
AdaBoost algorithm now works with multiple classes.
AdaBoost multiclass algorithm is available with SAMME and SAMME.R methods.AdaBoost, BrownBoost, and LogitBoost work with algorithms that support weights.
Improved performance for LBFGS Optimization Solver.
Started Neural Network Deprecation:
from Intel® DAAL 2020, Neural Networks will not have any new features
and functionalities. The support will be completely discontinued from
Intel® DAAL 2021. For more information, see the Deprecation Notes.