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Prediction Studio Help
Getting started
Prediction Studio overview
Setting up your environment
Setting access to Prediction Studio
Changing your workspace
Specifying a database for Prediction Studio records
Specifying a repository for text analytics models
Configuring the default rule context
Enabling outcome inferencing
Configuring the model transparency policy for predictive models
Configuring a machine learning service connection
Viewing Prediction Studio notifications
Analyzing example projects and models in Prediction Studio
Clearing deleted models in Prediction Studio
Developing and managing models
Adaptive analytics
Creating an adaptive model
Adding adaptive model predictors
Adding a predictor to an adaptive model
Adding multiple predictors to an adaptive model
Adding parameterized predictors
Enabling predefined Interaction History predictors for existing adaptive models
Defining outcome values in an adaptive model
Configuring Adaptive Model settings
Adaptive models monitoring
Viewing summarized adaptive model reports
Generating and downloading a model report
Viewing a model report
Model report details
Predictor report details
Viewing the predictors overview
Predictor performance information
Predictive analytics
Types of predictive models
Template categories for creating predictive models
Developing a predictive model
Creating a predictive model
Importing a PMML model
Configuring a PMML model
Connecting to a machine learning as a service model
Metadata file specification for predictive models
Configuring a predictive model
Editing settings for a predictive model
Outcome definition settings
Sample construction settings
Data analysis settings
Predictor grouping settings
Genetic algorithm settings
Score distribution settings
Preparing data
Selecting a data source
Constructing a sample
Defining an outcome
Defining the outcome field for scoring and extended scoring models
Defining a previous decision
Defining the outcome field for spectrum models
Virtual fields
Adding virtual fields
Modifying a virtual field
Deleting a virtual field
Analyzing data
Defining the predictor role
Analyzing and configuring predictors
Raw distribution
Binning predictors
Binning numeric predictors
Binning symbolic predictors
Grouping options for predictors
Auto grouping option for predictors
Managing grouping options for numeric predictors manually
Managing grouping options for symbolic predictors manually
Outcome inferencing
Checking distribution of accepts
Constructing an inference sample
Checking similarity based inference
Checking inference tuning
Comparing inference results
Performing final data analysis
Generating data analysis reports
Developing models
Grouping predictors
Creating models
Computation models
Creating a regression model
Creating a decision tree model
Creating a bivariate model
Creating a genetic algorithm model
Benchmark models
Sensitivity of models
Analyzing models
Comparing scores generated by models
Checking score distribution
Comparing the classification of scores generated by models
Selecting a model for deployment
Predictive models monitoring
Metrics for measuring predictive performance
Monitoring a predictive model
Viewing a predictive model report
Creating a custom template for creating a predictive model
Exporting a project
Importing a project
Text analytics
Creating text categorization models
Creating machine-learning topic detection models
Setting up a topic detection model
Preparing data for topic detection
Uploading data for training and testing of the topic detection model
Defining the training and testing samples for topic detection
Creating the topic detection model
Reviewing the topic detection model
Saving the topic detection model
Creating keyword-based topic detection models
Creating rules for keyword-based topic detection models
Defining a taxonomy
Configuring sentiment analysis
Setting up a sentiment analysis model
Preparing data for sentiment analysis
Uploading data for training and testing of the sentiment analysis model
Defining the training set and training the sentiment analysis model
Reviewing the sentiment analysis model
Saving the sentiment analysis model
Configuring intent detection
Setting up an intent detection model
Preparing data for intent detection
Uploading data for training and testing of the intent detection model
Defining training and testing samples, and building the intent detection model
Accessing intent analysis model evaluation reports
Saving the intent detection model
Configuring text extraction
Creating entity models
Building machine-learning text extraction models
Preparing data for text extraction
Defining the training set and training the text extraction model
Accessing text extraction model evaluation reports
Saving the text extraction model
Managing text analytics models
Managing incomplete text analytics models
Managing complete text analytics models
Migrating text analytics models
Exporting text analytics models
Importing text analytics models
Updating training data for text analytics models
Testing text analytics models
Sentiment lexicons
Text analytics accuracy measures
Managing data
Creating data sets
Creating summaries
Accessing text analytics resources