Scikit-learn-intelex

What's New in Intel® daal4py 2020 Update 3:

Introduced new daal4py functionality:
- Conversion of trained XGBoost* and LightGBM* models into a daal4py Gradient Boosted Trees model for fast prediction
- Support of Modin* DataFrame as an input
- Brute Force method for k-Nearest Neighbors classification algorithm, which for datasets with more than 13 features demonstrates a better performance than the existing K-D tree method
- k-Nearest Neighbors search for K-D tree and Brute Force methods with computation of distances to nearest neighbors and their indices

Extended existing daal4py functionality:
- Voting methods for prediction in k-Nearest Neighbors classification and search: based on inverse-distance and uniform weighting
- New parameters in Decision Forest classification and regression: minObservationsInSplitNode, minWeightFractionInLeafNode, minImpurityDecreaseInSplitNode, maxLeafNodes with best-first strategy and sample weights
- Support of Support Vector Machine (SVM) decision function for Multi-class Classifier

Improved daal4py performance for the following algorithms:
- SVM training and prediction
- Decision Forest classification training
- RBF and Linear kernel functions

Introduced new functionality for scikit-learn patching through daal4py:
- Acceleration of KNeighborsClassifier scikit-learn estimator with Brute Force and K-D tree methods
- Acceleration of RandomForestClassifier and RandomForestRegressor scikit-learn estimators
- Sparse input support for KMeans and Support Vector Classification (SVC) scikit-learn estimators
- Prediction of probabilities for SVC scikit-learn estimator
- Support of ‘normalize’ parameter for Lasso and ElasticNet scikit-learn estimators

Improved performance of the following functionality for scikit-learn patching through daal4py:
- train_test_split()
- Support Vector Classification (SVC) fit and prediction

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

**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

**Introduced new functionality**:
* Elastic Net algorithm with L1 and L2 regularization in batch computation mode. The algorithm supports various optimization solvers that handle non-smooth functions.
* Probabilistic classification for Decision Forest Classification algorithm with a choice voting method to calculate probabilities.

**Extended existing functionality**:
* Performance optimizations for distributed Spark samples, K-means algorithm for some input dimensions, Gradient Boosted Trees training stage for large datasets on multi-core platforms and Decision Forest prediction stage for datasets with a small number of observations on processors that support Intel® Advanced Vector Extensions 2 (Intel® AVX2) and Intel® Advanced Vector Extensions 512 (Intel® AVX-512)
* Performance optimizations across algorithms that use SOA (Structure Of Arrays) NumericTable as an input on processors that support Intel® Advanced Vector Extensions 512 (Intel® AVX-512)

Added support for Brownboost, Logistboost as well as Stump regression and Stump classification algorithms to daal4py.
Added support for Adaboost classification algorithm, including support for method="SAMME" or "SAMMER" for multi-class data.
"Variable Importance" feature has been added in Gradient Boosting Trees.
Ability to compute class prediction probabilities has been added to appropriate classifiers, including logistic regression, tree-based classifiers, etc.

Single node support for DBSCAN, LASSO, Coordinate Descent (CD) solver algorithms
Distributed model support for SVD, QR, K-means init++ and parallel++ algorithms

Product release with Intel(R) Parallel Studio 2019 Update 3