.. ****************************************************************************** .. * Copyright 2020 Intel Corporation .. * .. * Licensed under the Apache License, Version 2.0 (the "License"); .. * you may not use this file except in compliance with the License. .. * You may obtain a copy of the License at .. * .. * http://www.apache.org/licenses/LICENSE-2.0 .. * .. * Unless required by applicable law or agreed to in writing, software .. * distributed under the License is distributed on an "AS IS" BASIS, .. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. .. * See the License for the specific language governing permissions and .. * limitations under the License. .. *******************************************************************************/ ########## Examples ########## .. include:: note.rst Below are examples on how to utilize daal4py for various usage styles. General usage ------------- Building models from Gradient Boosting frameworks - `XGBoost* model conversion `_ - `LightGBM* model conversion `_ - `CatBoost* model conversion `_ Principal Component Analysis (PCA) Transform - `Single-Process PCA `_ - `Multi-Process PCA `_ Singular Value Decomposition (SVD) - `Single-Process PCA Transform `_ - `Single-Process SVD `_ - `Streaming SVD `_ - `Multi-Process SVD `_ Moments of Low Order - `Single-Process Low Order Moments `_ - `Streaming Low Order Moments `_ - `Multi-Process Low Order Moments `_ Correlation and Variance-Covariance Matrices - `Single-Process Covariance `_ - `Streaming Covariance `_ - `Multi-Process Covariance `_ Decision Forest Classification - `Single-Process Decision Forest Classification Default Dense method `_ - `Single-Process Decision Forest Classification Histogram method `_ Decision Tree Classification - `Single-Process Decision Tree Classification `_ Gradient Boosted Classification - `Single-Process Gradient Boosted Classification `_ k-Nearest Neighbors (kNN) - `Single-Process kNN `_ Multinomial Naive Bayes - `Single-Process Naive Bayes `_ - `Streaming Naive Bayes `_ - `Multi-Process Naive Bayes `_ Support Vector Machine (SVM) - `Single-Process Binary SVM `_ - `Single-Process Muticlass SVM `_ Logistic Regression - `Single-Process Binary Class Logistic Regression `_ - `Single-Process Logistic Regression `_ Decision Forest Regression - `Single-Process Decision Forest Regression Default Dense method `_ - `Single-Process Decision Forest Regression Histogram method `_ - `Single-Process Decision Tree Regression `_ Gradient Boosted Regression - `Single-Process Boosted Regression `_ Linear Regression - `Single-Process Linear Regression `_ - `Streaming Linear Regression `_ - `Multi-Process Linear Regression `_ Ridge Regression - `Single-Process Ridge Regression `_ - `Streaming Ridge Regression `_ - `Multi-Process Ridge Regression `_ K-Means Clustering - `Single-Process K-Means `_ - `Multi-Process K-Means `_ Multivariate Outlier Detection - `Single-Process Multivariate Outlier Detection `_ Univariate Outlier Detection - `Single-Process Univariate Outlier Detection `_ Optimization Solvers-Mean Squared Error Algorithm (MSE) - `MSE In Adagrad `_ - `MSE In LBFGS `_ - `MSE In SGD `_ Logistic Loss - `Logistic Loss SGD `_ Stochastic Gradient Descent Algorithm - `Stochastic Gradient Descent Algorithm Using Logistic Loss `_ - `Stochastic Gradient Descent Algorithm Using MSE `_ Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm - `Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm - Using MSE `_ Adaptive Subgradient Method - `Adaptive Subgradient Method Using MSE `_ Cosine Distance Matrix - `Single-Process Cosine Distance `_ Correlation Distance Matrix - `Single-Process Correlation Distance `_ Trees - `Decision Forest Regression `_ - `Decision Forest Classification `_ - `Decision Tree Regression `_ - `Decision Tree Classification `_ - `Gradient Boosted Trees Regression `_ - `Gradient Boosted Trees Classification `_