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---
title: Visual AI reference
description: Read a brief overview and reference describing the technological components of Visual AI.
---
# Visual AI reference {: #visual-ai-reference }
The following sections provide a very brief overview on the technological components of Visual AI.
A common approach for modeling i... | vai-ref |
---
title: Visual AI reference
description: Do a deep dive on DataRobot's Visual AI.
---
# Visual AI reference {: #visual-ai-reference }
These sections describe the workflow and reference materials for including images as part of your DataRobot project.
Topic | Describes...
----- | ------
[Visual AI reference](vai-... | index |
---
title: Visual AI tuning guide
description: Step through several recommended methods for maximizing Visual AI classification accuracy.
---
# Visual AI tuning guide {: #visual-ai-tuning-guide }
In this guide, you will step through several recommended methods for maximizing Visual AI classification accuracy with a b... | vai-tuning-guide |
---
title: Document AI insights
description: Use the Document AI visualizations to better understand the information contained in your documents.
section_name: AutoML
maturity: public-preview
---
# Document AI insights {: #document-ai-insights }
DataRobot provides a variety of visualizations to help better understand... | doc-ai-insights |
---
title: Document ingest and modeling
description: Learn how to ingest PDF documents as an input to modeling.
section_name: AutoML
maturity: public-preview
---
# Document ingest and modeling {: #document-ingest-and-modeling }
PDF documents used for modeling are extracted by tasks within the blueprint and registered... | doc-ai-ingest |
---
title: Document AI
description: Learn how to use documents as a data source without manual intervention to make documents available for modeling.
section_name: AutoML
maturity: public-preview
---
# Document AI {: #document-ai }
!!! info "Availability information"
Document AI modeling is off by default. Contac... | index |
---
title: Predictions from documents
description: Multiple methods are available for making predictions with Document AI.
section_name: AutoML
maturity: public-preview
---
# Predictions from documents {: #predictions-from-documents }
The following prediction methods are available for Document AI.
Method | Descript... | doc-ai-predictions |
---
title: Document AI overview
description: Read background information and a simplified workflow overview.
section_name: AutoML
maturity: public-preview
---
# Document AI overview {: #document-ai-overview }
Analysts and data scientists often want to use the information contained in PDF documents to build models. H... | doc-ai-overview |
---
title: Comments
description: With the Comments link, you can add comments to, or host a discussion around, any item you have access to in the catalog.
---
# Comments {: #comments }
{% include 'includes/comm-add.md' %}
| index |
---
title: Word Cloud
description: Word Cloud displays the most relevant words and short phrases in word cloud format.
---
# Word Cloud {: #word-cloud }
Text variables often contain words that are highly indicative of the response. The **Word Cloud** insight displays up to the 200 most impactful words and short phra... | word-cloud |
---
title: Feature Effects
description: Feature Effects (with partial dependence) conveys how changes to the value of each feature change model predictions.
---
# Feature Effects {: #feature-effects }
!!! warning
**Evaluate > Feature Fit** has been removed. Use **Feature Effects** instead, as it provides the same ... | feature-effects |
---
title: Understand tabs
description: The Understand tabs—Feature Effects, Feature Impact, Prediction Explanations, and Word Cloud—explain what drives a model’s predictions.
---
# Understand {: #understand }
The **Understand** tabs explain what drives a model’s predictions:
Leaderboard tabs | Description | Sourc... | index |
---
title: Feature Impact
description: Feature Impact shows, on demand, which features are driving model decisions the most. It is rendered using permutation, SHAP, or tree-based importance.
---
# Feature Impact {: #feature-impact }
!!! note
To retrieve the SHAP-based **Feature Impact** visualization, you must e... | feature-impact |
---
title: Cluster Insights
description: Learn how the Cluster Insights visualization helps you to understand the natural groupings in your data.
---
# Cluster Insights {: #cluster-insights }
With the Cluster Insights visualization, you can understand and name each cluster in a dataset. Use clustering to capture a l... | cluster-insights |
---
title: Insights
description: The Insights tab lets you view and analyze visualizations for your project, switching between models to make comparisons.
---
# Insights {: #insights }
The **Insights** tab lets you [view and analyze visualizations](#work-with-insights) for your project, switching between models to m... | analyze-insights |
---
title: Bias vs Accuracy
description: The Bias vs Accuracy chart compares predictive accuracy and fairness, removing the need to manually note each model's accuracy and fairness scores.
---
# Bias vs Accuracy {: #bias-vs-accuracy }
The **Bias vs Accuracy** chart shows the tradeoff between predictive accuracy and ... | bias-tab |
---
title: Learning Curves
description: Use the Learning Curve graph to help determine whether getting additional data would be worth the expense if it increases model accuracy.
---
# Learning Curves {: #learning-curves }
Use the **Learning Curve** graph to help determine whether it is worthwhile to increase the siz... | learn-curve |
---
title: Other Leaderboard tabs
description: In addition to the model-specific tabs available, additional insights are available on the Leaderboard level.
---
# Other {: #other }
In addition to the model-specific tabs available, additional insights are available on the Leaderboard level:
Insight tab | Descriptio... | index |
---
title: Model Comparison
description: You can compare the business returns of built models in a project using the Model Comparison tab or the Leaderboard's Compare Selected.
---
# Model Comparison {: #model-comparison }
Comparing Leaderboard models can help identify the model that offers the highest business retu... | model-compare |
---
title: Speed vs Accuracy
description: Predictive accuracy often requires longer prediction runtime. The Speed vs Accuracy plot shows the runtime/accuracy tradeoff to help you choose the best model.
---
# Speed vs Accuracy {: #speed-vs-accuracy }
Predictive accuracy often comes at the price of increased predictio... | speed |
---
title: Model Compliance
description: Details and steps to generate the Model Compliance Document.
---
# Model Compliance {: #model-compliance }
DataRobot automates many critical compliance tasks associated with developing a model and, by doing so, [decreases the time-to-deployment](#generalized-model-validation-... | compliance |
---
title: Compliance
description: The Compliance tabs compile model development documentation that can be used for regulatory validation.
---
# Compliance {: #compliance }
!!! info "Availability information"
Availability of compliance documentation is dependent on your configuration. Contact your DataRobot rep... | index |
---
title: Template Builder for compliance reports
description: For regulated industries, how to use Template Builder to generate required documentation using the provided compliance template or a custom template.
---
# Template Builder {: #template-builder }
In some regulated industries, models have to go through a... | template-builder |
---
title: Coefficients (preprocessing)
description: How to use the Coefficients tab, which shows the positive or negative impact of important variables, to help you refine and optimize your models.
---
# Coefficients (preprocessing) {: #coefficients-preprocessing }
The **Coefficients** tab provides a visual indicat... | coefficients |
---
title: Rating Tables
description: How to display a model’s Rating Table tab, and export the model's validated parameters. Validation ensures correct parameters and reproducible results.
---
# Rating Tables {: #rating-tables }
When a model displays the rating table  icon on the Leaderb... | rating-table |
---
title: Model Info
description: To view the Model Info tab, click a model on the Leaderboard, then click Model Info. The tab’s tiles report general model and performance information.
---
# Model Info {: #model-info }
To display model information, click a model on the Leaderboard list then click **Model Info**. T... | model-info |
---
title: Model log
description: How to display the model log, which shows the status of successful operations with green INFO tags and errors marked with red ERROR tags.
---
# Log {: #log }
The model log displays the status of successful operations with green INFO tags, along with information about errors marked w... | log |
---
title: Describe
description: Introduces the Leaderboard tabs, including Blueprint, Coefficients, Constraints, Data Quality Handling Reports, Eureqa Models, Log, Model Info, and Rating Table.
---
# Describe {: #describe }
The **Describe** tabs provide model building information and feature details:
Leaderboard t... | index |
---
title: Eureqa Models
description: The Eureqa Models tab lets you inspect and compare the best models generated from a Eureqa blueprint, to balance predictive accuracy against complexity.
---
# Eureqa Models {: #eureqa-models }
The **Eureqa Models** tab provides access to model blueprints for Eureqa generalized a... | eureqa |
---
title: Monotonic constraints
description: How to force an XGBoost model to learn only monotonic (always increasing or always decreasing) relationships between chosen features and the target.
---
# Monotonic constraints {: #monotonic-constraints }
In some projects (typically insurance and banking), you may want t... | monotonic |
---
title: GA2M output (from Rating Tables)
description: Overview and detailed explanations of the output for Generalized Additive Model (GA2M) models, available for download from the Rating Tables tab.
---
# GA2M output (from Rating Tables) {: #ga2m-output-from-rating-tables }
The following section helps to underst... | ga2m |
---
title: Data Quality Handling Report
description: How to use the Data Quality Handling Report, which reports on tasks and imputation methods.
---
# Data Quality Handling Report {: #data-quality-handling-report }
The Data Quality Handling Report can be found in a model's **Describe** division.
description: Available for multiseries projects, the Series Insights tab provides series-specific information in both charted and tabular format.
---
# Series Insights (multiseries) {: #series-insights-multiseries }
The **Series Insights** tab for time series [multiseries](mu... | series-insights-multi |
---
title: Anomaly visualizations
description: During unsupervised learning on time series, anomaly visualizations help to locate and analyze anomalies that occur across the timeline of your data.
---
# Anomaly visualizations {: #anomaly-visualizations }
For time series [anomaly detection](anomaly-detection), DataRo... | anom-viz |
---
title: Residuals
description: The Residuals tab helps you understand the predictive performance and validity of a regression model by letting you gauge how linearly your model scales.
---
# Residuals {: #residuals }
The Residuals tab is designed to help you clearly understand the predictive performance and valid... | residuals |
---
title: Training Dashboard
description: Use the Training Dashboard to view a model's training and test loss, accuracy (for some projects), learning rate, and momentum, to learn how training went.
---
# Training Dashboard {: #training-dashboard }
!!! note
The Training Dashboard tab is currently available for K... | training-dash |
---
title: Forecast vs Actual
description: How to use Forecast vs Actual, which allows you to compare how different predictions behave from different forecast points to different times in the future.
---
# Forecast vs Actual {: #forecast-vs-actual }
Time series forecasting predicts multiple values for each point in ... | fore-act |
---
title: Forecasting Accuracy
description: The Forecasting Accuracy tab provides a visual indicator of how well a model predicts at each forecast distance in the project's forecast window.
---
# Forecasting Accuracy {: #forecasting-accuracy }
The **Forecasting Accuracy** tab provides a visual indicator of how well... | forecast-acc |
---
title: Series Insights (clustering)
description: Available for clustering projects, the Series Insights tab provides series clustering information in both charted and tabular format.
---
# Series Insights (clustering) {: #series-insights-clustering }
The **Series Insights** tab for time series [clustering](ts-cl... | series-insights |
---
title: Lift Chart
description: The Lift Chart depicts how well a model segments the target population and how capable it is of predicting the target, to show the model's effectiveness.
---
# Lift Chart {: #lift-chart }
The Lift Chart depicts how well a model segments the target population and how capable it is o... | lift-chart |
---
title: Evaluate
description: The Evaluate tabs provide key plots and statistics needed to judge a model's effectiveness, including ROC Curve, Lift Chart, and Forecasting Accuracy.
---
# Evaluate {: #evaluate }
The **Evaluate** tabs provide key plots and statistics needed to judge and interpret a model’s effectiv... | index |
---
title: Stability
description: The Stability tab provides an at-a-glance summary of how well a model performs on different backtests, to understand whether a model is consistent across time.
---
# Stability {: #stability }
The **Stability** tab provides an at-a-glance summary of how well a model performs on diffe... | stability |
---
title: Advanced Tuning
description: How to create models with Advanced Tuning, which lets you manually set model parameters to override the DataRobot selections and create a named “tune.”
---
# Advanced Tuning {: #advanced-tuning }
Advanced tuning allows you to manually set model parameters, overriding the DataRo... | adv-tuning |
---
title: Accuracy Over Time
description: How to use the Accuracy Over Time tab, which becomes available when you specify date/time partitioning, to visualize how predictions change over time.
---
# Accuracy Over Time {: #accuracy-over-time }
The **Accuracy Over Time** tab helps to visualize how predictions change ... | aot |
---
title: Confusion Matrix (for multiclass models)
description: The multiclass confusion matrix compares actual and predicted data values, so you can see if any mislabeling has occurred and with which values.
---
# Confusion Matrix (for multiclass models) {: #confusion-matrix-for-multiclass-models }
!!! info "Avail... | multiclass |
---
title: Per-Class Bias
description: How to use Per-Class Bias, which helps to identify if a model is biased, and if so, how much and whom it's biased towards or against.
---
# Per-Class Bias {: #per-class-bias }
**Per-Class Bias** helps to identify *if* a model is biased, and if so, *how much* and *who* it's bias... | per-class |
---
title: Cross-Class Accuracy
description: How to use the Cross-Class Accuracy table to understand the model's accuracy performance for each protected class.
---
# Cross-Class Accuracy {: #cross-class-accuracy }
The **Cross-Class Accuracy** tab calculates, for each protected feature, evaluation metrics and ROC cur... | cross-acc |
---
title: Bias and fairness
description: Introduces the Bias and Fairness tabs, which identify if a model is biased and why the model is learning bias from the training data.
---
# Bias and Fairness {: #bias-and-fairness }
The **Bias and Fairness** tabs identify if a model is biased and why the model is learning bi... | index |
---
title: Cross-Class Data Disparity
description: How to use the Cross-Class Data Disparity insight, which shows why the model is biased, and where in the training data it learned the bias from.
---
# Cross-Class Data Disparity {: #cross-class-data-disparity }
The **Cross-Class Data Disparity** insight shows *why* ... | cross-data |
---
title: Portable Predictions
description: Learn about DataRobot's available methods for portable predictions.
---
# Portable Predictions {: #portable-predictions}
{% include 'includes/port-pred-options.md' %} | port-pred |
---
title: Deploy tab
description: How to deploy a model from the Leaderboard
---
# Deploy tab {: #deploy-tab }
You can deploy models you build with DataRobot AutoML from the Leaderboard. In most cases, before deployment, you should unlock holdout and [retrain your model](creating-addl-models#retrain-a-model) at 100... | deploy |
---
title: Predict
description: Details on the Leaderboard Predict tab's capabilities.
---
# Predict {: #predict }
The **Predict** tab allows you to download various model assets and test predictions. For more information about the predictions methods in DataRobot, see [Predictions Overview](../../../predictions/in... | index |
---
title: Make predictions before deploying a model
description: Learn how to make predictions on models that are not yet deployed and how to make predictions using an external dataset or your training data.
---
# Make predictions before deploying a model {: #make-predictions-before-deploying-a-model }
This section... | predict |
---
title: Downloads tab
description: Understand how to use the Downloads tab to export models for transfer and download exportable charts.
---
# Downloads tab {: #downloads-tab }
The **Downloads** tab allows you to download model artifacts—chart/graph PNGs and model data—in a single ZIP file. To access... | download |
---
title: Text Prediction Explanations
description: Helps to understand the importance (both negative and positive impacts) a model places on words and phrases.
---
# Text Prediction Explanations {: #text-prediction-explanations }
DataRobot has several visualizations that help to understand which features are most p... | predex-text |
---
title: XEMP Prediction Explanations
description: To view XEMP-based Prediction Explanations, which work for all models, first calculate feature impact on the Prediction Explanations or Feature Impact tabs.
---
# XEMP Prediction Explanations {: #xemp-prediction-explanations }
This section describes XEMP-based Pre... | xemp-pe |
---
title: Prediction Explanations
description: Index page for SHAP, XEMP, and Text Prediction Explanations.
---
# Prediction Explanations {: #prediction-explanations }
The following sections provide an overview and describe the alternate methodologies for working with Prediction Explanations:
Topic | Describes...... | index |
---
title: Prediction Explanations overview
description: SHAP and XEMP Prediction Explanations give a quantitative indicator of how variables affect predictions by row. Text explanations identify which specific words within a feature are impactful.
---
# Prediction Explanations overview {: #prediction-explanations-ov... | predex-overview |
---
title: SHAP Prediction Explanations
description: Enable SHAP-based Prediction Explanations prior to building tree- and linear-based models to understand which features drive each model decision.
---
# SHAP Prediction Explanations {: #shap-prediction-explanations }
!!! note
This section describes SHAP-based P... | shap-pe |
---
title: Profit curve
description: The ROC Curve tab in DataRobot lets you generate profit curves that help you estimate the business impact of a selected model.
---
# Profit curve {: #profit-curve }
Like the other visualization tools on the **[ROC Curve](roc-curve-tab-use)** tab, profit curves are available for b... | profit-curve |
---
title: Cumulative Charts
description: Cumulative Charts available in the DataRobot ROC Curve tab help you to assess model performance by exploring the model's cumulative characteristics.
---
# Cumulative charts {: #cumulative-charts }
The Chart pane (on the **[ROC Curve](roc-curve-tab-use)** tab) allows you to g... | cumulative-charts |
---
title: Select data and display threshold
description: Thresholds in the ROC Curve tab in DataRobot set the class boundary for a predicted value. The display threshold updates the visualizations and the prediction threshold changes the threshold for all predictions made using the model.
---
# Select data and displ... | threshold |
---
title: Custom charts
description: The ROC Curve tab lets you create custom charts that help you explore classification, performance, and statistics related to a selected machine learning model.
---
# Custom charts {: #custom-charts }
The Chart pane in the **[ROC Curve](roc-curve-tab-use)** tab allows you to crea... | custom-charts |
---
title: Prediction Distribution graph
description: The Prediction Distribution graph on the ROC Curve tab helps you evaluate classification models by showing the distribution of actual values in relation to the prediction threshold.
---
# Prediction Distribution graph {: #prediction-distribution-graph }
The Pred... | pred-dist-graph |
---
title: Confusion matrix
description: The confusion matrix available on the DataRobot ROC Curve tab lets you evaluate accuracy by comparing actual versus predicted values.
---
# Confusion matrix {: #confusion-matrix }
The **[ROC Curve](roc-curve-tab-use)** tab provides a confusion matrix that lets you evaluate ac... | confusion-matrix |
---
title: ROC Curve tools
description: The ROC Curve tools help you explore classification, performance, and statistics related to a selected model at any point on the probability scale.
---
# ROC Curve tools {: #roc-curve-tools }
The **ROC Curve** tab provides tools for exploring classification, performance, and st... | index |
---
title: ROC curve
description: The ROC curve visualization in DataRobot helps you explore classification, performance, and statistics for a selected model. ROC curves plot the true positive rate against the false positive rate for a given data source.
---
# ROC curve {: #roc-curve }
The ROC curve visualization (o... | roc-curve |
---
title: Metrics
description: The Metrics pane in the DataRobot ROC Curve tab helps you explore statistics related to a selected machine learning model.
---
# Metrics {: #metrics }
The Metrics pane, on the bottom right of the **[ROC Curve](roc-curve-tab-use)** tab, contains standard statistics that DataRobot provi... | metrics |
---
title: Use the ROC Curve tools
description: Learn how to access the visualization tools available on the ROC Curve tab.
---
# Use the ROC Curve tools {: #use-the-roc-curve-tools }
The **ROC Curve** tools provide visualizations and metrics to help you determine whether the classification performance of a particula... | roc-curve-tab-use |
---
title: Multiseries segmentation visual overview
dataset_name: N/A
Description: Provides a list of frequently asked questions, and brief answers about multiseries modeling with segmentation in DataRobot. Answers link to more complete documentation.
domain: time series
expiration_date: 3-10-2022
owner: anatolii.stehn... | segmented-qs |
---
title: Segmented modeling FAQ
dataset_name: N/A
Description: Provides a list of frequently asked questions, and brief answers about multiseries modeling with segmentation in DataRobot. Answers link to more complete documentation.
domain: time series
expiration_date: 3-10-2022
owner: anatolii.stehnii@datarobot.com
u... | segmented-faq |
---
title: Time-aware considerations
description: This page describes considerations to be aware of when working with DataRobot time series modeling.
---
# Time-aware considerations {: #time-aware-considerations }
Both time-aware modeling mechanisms—OTV and automated time series—are implemented using [da... | ts-consider |
---
title: Time series reference
description: This section provides deep-dive reference material for DataRobot time series modeling.
---
# Time series reference {: #time-series-reference }
This section provides deep-dive reference material for DataRobot time series modeling.
Topic | Describes...
----- | ------
[Tim... | index |
---
title: Autopilot in time-aware projects
description: DataRobot's modeling modes are different in time series projects where the modeling mode defines the set of blueprints run but not the amount data to train on.
---
# Autopilot in time-aware projects {: #autopilot-in-time-aware-projects }
!!! note
See the [A... | multistep-ta |
---
title: Time series framework
description: Gain a deeper understanding of the framework DataRobot uses to build time series models.
---
# Time series framework {: #time-series-framework }
This section describes the basic time series framework, window-created gaps, and common data patterns for time series problems.... | ts-framework |
---
title: Time series feature derivation
description: A comprehensive reference of the DataRobot time series feature derivation process.
---
# Time series feature derivation {: #time-series-feature-derivation }
The following tables document the feature derivation process—operators used and feature names creat... | feature-eng |
---
title: Clustering algorithms
Description: A deep dive into the DTW, Velocity, and K-means algorithms used in clustering.
---
# Clustering algorithms {: #clustering-algorithms }
Clustering is the ability to cluster time series within a multiseries dataset and then directly apply those clusters as segment IDs with... | clustering-algos |
---
title: Time series feature lists
description: Understand DataRobot's feature lists that are specialized for time series modeling.
---
# Time series feature lists {: #time-series-feature-lists }
DataRobot automatically constructs time series features based on the characteristics of the data (e.g., stationarity and... | ts-feature-lists |
---
title: Batch predictions for TTS and LSTM models
description: Make batch predictions for Traditional Time Series (TTS) and Long Short-Term Memory (LSTM) models.
section_name: Time Series
maturity: public-preview
---
# Batch predictions for TTS and LSTM models {: #batch-predictions-for-tts-and-lstm-models }
!!! in... | pp-ts-tts-lstm-batch-pred |
---
title: Period Accuracy
description: Period Accuracy provides the ability to compute error metric values for specific periods of the backtest validation source.
section_name: Time Series
maturity: public-preview
---
# Period Accuracy {: #period-accuracy }
!!! info "Availability information"
The Period Accurac... | ts-period-accuracy |
---
title: Time series public preview features
description: Read preliminary documentation for time series features currently in the DataRobot public preview pipeline.
section_name: Time Series
maturity: public-preview
---
# Time series public preview features {: #time-series-public-preview-features }
{% include 'in... | index |
---
title: Time series model package prediction intervals
description: Export time series models with prediction intervals in model package (.mlpkg) format.
section_name: Time Series
maturity: public-preview
---
# Time series model package prediction intervals {: #time-series-model-package-prediction-intervals }
!!! ... | pp-ts-pred-intervals-mlpkg |
---
title: Create the modeling dataset
description: Understand how DataRobot's feature derivation process creates a new modeling dataset for time series projects.
---
# Create the modeling dataset {: #create-the-modeling-dataset }
The time series modeling framework extracts relevant features from time-sensitive data... | ts-create-data |
---
title: Data prep for time series
description: For time series projects, DataRobot's data quality detection evaluates whether the time step is irregular and provides tools to correct the dataset.
---
# Data prep for time series {: #data-prep-for-time-series }
When starting a time series project, DataRobot's data q... | ts-data-prep |
---
title: Restore features removed by reduction
description: DataRobot then runs a feature reduction algorithm, removing features it detects as low impact, but you can add these features back into your available derived modeling data.
---
# Restore features removed by reduction {: #restore-features-removed-by-reducti... | restore-features |
---
title: Time series modeling data
description: This topic describes the creation and management of the modeling dataset that is a result of the feature derivation process.
---
# Time series modeling data {: #time-series-modeling-data }
This topic describes the creation and management of the modeling dataset that ... | index |
---
title: Date/time partitioning advanced options
description: Date/time partitioning sets up the underlying structure that supports time-aware modeling.
---
# Date/time partitioning advanced options {: #configure-ts-date-time-partitioning-advanced-options }
DataRobot's default partitioning settings are optimized fo... | ts-date-time |
---
title: Customizing time series projects
description: Describes how DataRobot calculates training partitions and the partitioning requirements for time series modeling.
---
# Customizing time series projects {: #customizing-time-series-projects }
DataRobot provides default window settings ([Feature Derivation](gl... | ts-customization |
---
title: Time series advanced modeling
description: This topic provides deep-dive reference material for DataRobot time series modeling.
---
# Time series advanced modeling {: #time-series-advanced-modeling }
This section provides information for DataRobot time series modeling.
Topic | Describes...
----- | ------... | index |
---
title: Time series advanced options
description: Describes the settings available from the Time Series advanced option tab, where you can set features known in advance, exponential trends, and differencing for time series projects.
---
# Time Series advanced options {: #time-series-advanced-options }
{% include ... | ts-adv-opt |
---
title: Clustering advanced options
description: Allows you to set the number of clusters that DataRobot automatically discovers during time series clustering.
---
# Clustering advanced options {: #clustering-advanced-options }
{% include 'includes/ts-cluster-adv-opt-include.md' %}
| ts-cluster-adv-opt |
---
title: Error metric guidance
description: Identifies the top Eureqa model error metrics for different types of problems.
---
# Error metric guidance {: #error-metric-guidance }
The good news is that there are common error metrics that work well on a large majority of problem types. Starting with one of these erro... | guidance |
---
title: Tune Eureqa models
description: Customize Eureqa models by modifying various Advanced Tuning parameters and creating custom target expressions.
---
# Tune Eureqa models {: #tune-eureqa-models }
You can customize Eureqa models by modifying various Advanced Tuning parameters and creating custom target expres... | advanced-options |
---
title: Error metrics
description: Eureqa error metrics measure how well a Eureqa model fits your data; DataRobot supports a variety of different error metrics for Eureqa models.
---
# Error metrics {: #error-metrics }
Eureqa error metrics are measures of how well a Eureqa model fits your data. When DataRobot perf... | error-metrics |
---
title: Custom target expressions
description: Describes the many ways to customize target expressions for Eureqa models.
---
# Custom target expressions {: #custom-target-expressions }
Customizing target expressions provides one way to custom tune Eureqa models. Expressions may be any nested combination of [Eureq... | custom-expressions |
---
title: Row weighting blocks
description: Use the Row Weighting parameter as part of tuning Eureqa models.
---
# Row weighting blocks {: #row-weighting-blocks }
You can configure row weight to help improve performance for your models. The Row Weighting parameter, **weight_expr**, is available within the Prediction... | row-weighting |
---
title: Eureqa advanced tuning
description: Eureqa models use expressions to represent mathematical relationships and transformations. DataRobot provides specialized workflows for tuning Eureqa models.
---
# Eureqa advanced tuning {: #eureqa-advanced-tuning }
Eureqa models use expressions to represent mathematica... | index |
---
title: Configure building blocks
description: Combine and configure building blocks to create a new Target Expression for Eureqa models.
---
# Configure building blocks {: #configure-building-blocks }
Building blocks are components of Eureqa models. As part of tuning a Eureqa model, you can combine and configure ... | building-blocks |
---
title: Building blocks reference
description: Provides the definitions and usage of all building blocks available to Eureqa models.
---
# Building blocks reference {: #building-blocks-reference }
This page provides the definitions and usage of all building blocks available to Eureqa models. To access further info... | eureqa-reference |
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