--- schema_version: "1" id: 9b49e62a440bee09e169694f298932bc33feb798f64208e946deaa96b832d9aa title: Edit General Settings for a Model collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/analytics/264-0-0/bi_edd_story_general.md canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_edd_story_general.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: 4be71111793b64bef9cf618d43f464b08ce827a025f4972bc6ee19d343ffef7a description: You can edit general settings for a model. View the CRM Analytics dataset and select the algorithm and validation type. products: - CRM Analytics - Cross Cloud release: "264" --- # Edit General Settings for a Model You can edit general settings for a model. View the CRM Analytics dataset and select the algorithm and validation type. ### Required Editions ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/images/icon_note.png) Note Einstein Discovery stories are now models. We wish we could snap our fingers to update the name everywhere, but you can expect to see the previous name in a few places until we replace it.

Available in Salesforce Classic and Lightning Experience.

Available with CRM Analytics, which is available for an extra cost in Enterprise, Performance, and Unlimited Editions. Also available in Developer Edition.

User Permissions Needed
To update model settings:	Create and Update Einstein Discovery Models

To edit general settings for a model: 1. Open the model. 2. Click **Settings**. 3. In the **General Settings** panel, configure the following settings. ![General settings in model setup](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/bi/images/bi/bi_edd_story_general.png)

Setting	Description
Dataset	CRM Analytics dataset associated with the model.
Validation	Choose a validation type for your model. Select one of the following options: Training/Validation Ratio to randomly split the observations in the CRM Analytics dataset into a training set and a validation set. The default is 80% training and 20% validation, but you can adjust the slider to change the ratio as needed. Validation Dataset to specify a separate CRM Analytics dataset that contains the validation data. You see only the datasets that match the schema of your model’s dataset. Ideally, select a dataset that contains a diverse set of observations that reflect real-world conditions. If you want to compare models for accuracy, selecting the same validation dataset gives you a common, unbiased baseline for evaluation. None (the default) uses only k-fold validation.
Algorithm	You can choose among different algorithms for Einstein Discovery to use to create the model: GLM: Default. Generalized Linear Model is a regression-based algorithm. GBM: Gradient Boost Machine is a decision tree-based ensemble machine learning algorithm. XGBoost: A decision tree-based, ensemble machine learning algorithm. Random Forest: A supervised learning algorithm that uses multiple decision trees, randomization, and other optimization techniques. Alternatively, select Model Tournament to have Einstein Discovery run all four algorithms and then show the results of the algorithm that performed best.

Setting

Description

Dataset

CRM Analytics dataset associated with the model.

Validation

Choose a validation type for your model. Select one of the following options:

Training/Validation Ratio to randomly split the observations in the CRM Analytics dataset into a training set and a validation set. The default is 80% training and 20% validation, but you can adjust the slider to change the ratio as needed.
Validation Dataset to specify a separate CRM Analytics dataset that contains the validation data. You see only the datasets that match the schema of your model’s dataset. Ideally, select a dataset that contains a diverse set of observations that reflect real-world conditions. If you want to compare models for accuracy, selecting the same validation dataset gives you a common, unbiased baseline for evaluation.
None (the default) uses only k-fold validation.

Algorithm

You can choose among different algorithms for Einstein Discovery to use to create the model:

GLM: Default. Generalized Linear Model is a regression-based algorithm.
GBM: Gradient Boost Machine is a decision tree-based ensemble machine learning algorithm.
XGBoost: A decision tree-based, ensemble machine learning algorithm.
Random Forest: A supervised learning algorithm that uses multiple decision trees, randomization, and other optimization techniques.

Alternatively, select Model Tournament to have Einstein Discovery run all four algorithms and then show the results of the algorithm that performed best.

Changes take effect after you create the model or a model version.