daal4py 2020.2

Introduced new functionality:

• Thunder method for Support Vector Machine (SVM) training algorithm, which demonstrates better training time than the existing sequential minimal optimization method

Extended existing functionality:

• Training with the number of features greater than the number of observations for Linear Regression, Ridge Regression, and Principal Component Analysis
• New sample_weights parameter for SVM algorithm
• New parameter in K-Means algorithm, resultsToEvaluate, which controls computation of centroids, assignments, and exact objective function

Improved performance for the following:

• Support Vector Machine training and prediction, Elastic Net and LASSO training, Principal Component Analysis training and transform, K-D tree based k-Nearest Neighbors prediction
• K-Means algorithm in batch computation mode
• RBF kernel function

Deprecated 32-bit support:

• 2020 product line will be the last one to support 32-bit

Introduced improvements to daal4py library:

• Performance optimizations for pandas input format
• Scikit-learn compatible API for AdaBoost classifier, Decision Tree classifier, and Gradient Boosted Trees classifier and regressor

Improved performance of the following Intel Scikit-learn algorithms and functions:

• fit and prediction in K-Means and Support Vector Classification (SVC), fit in Elastic Net and LASSO, fit and transform in PCA
• Support Vector Classification (SVC) with non-default weights of samples and classes
• train_test_split() and assert_all_finite()

To install this package with conda run the following:
conda install -c intel daal4py