Glossary

<!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You 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. -->

Data Preparator

• Part of Engine. It reads data from source and transforms it to the desired format.

Data Source

• Part of Engine. It preprocesses the data and forward it to the algorithm for model training.

Engine

• An Engine represents a type of prediction, e.g. product recommendation. It is comprised of four components: [D] Data Source and Data Preparator, [A] Algorithm, [S] Serving, [E] Evaluation Metrics.

EngineClient

• Part of PredictionSDK. It sends queries to a deployed engine instance through the Engine API and retrieves prediction results.

Event API

• Please see Event Server.

Event Server

• Event Server is designed to collect data into PredictionIO in an event-based style. Once the Event Server is launched, your application can send data to it through its Event API with HTTP requests or with the EventClient of PredictionIO's SDKs.

EventClient

• Please see Event Server.

Live Evaluation

• Evaluation of prediction results in a production environment. Prediction results are shown to real users. Users do not rate the results explicitly but the system observes user behaviors such as click through rate.

Offline Evaluation

• The prediction results are compared with pre-compiled offline datasets. Typically, offline evaluations are meant to identify the most promising approaches.

Test Data

• Also commonly referred as Test Set. A set of data used to assess the strength and utility of a predictive relationship.

Training Data

• Also commonly referred as Training Set. A set of data used to discover potentially predictive relationships. In PredictionIO Engine, training data is processed through the Data layer and passed onto algorithm.