# Model insights
URL: https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/index.html

> Introduces the many insights the DataRobot Leaderboard provides when you select a model, with links to details.

# Model insights

When you select a model, DataRobot makes available a large selection of insights, grouped by purpose, appropriate for that model.

## Model Leaderboard

The model Leaderboard is a list of models ranked by the chosen performance metric, with the best models at the top of the list. It provides a [variety of insight tabs](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/index.html#leaderboard-tabs), available based on user permissions and applicability. Hover over an inactive division to view a dropdown of member tabs.

> [!NOTE] Note
> Tabs are visible only if they are applicable to the project type. For example, time series-related tabs (e.g., Accuracy Over Time) only display for time series projects. Tabs that are applicable to a project but not a particular model type display as grayed out (for example, [blender](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/leaderboard-ref.html#blender-models) models, due to the nature of their construction, have fewer tab functions available).

The pages within this section provide information on using and interpreting the insights available from the Leaderboard ( Models tab). See the [Leaderboard reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/leaderboard-ref.html) for information on the badges and components of the Leaderboard as well as functions such as tagging, searching, and exporting data.

## Leaderboard tabs

| Tab name | Description |
| --- | --- |
| Evaluate: Key plots and statistics for judging model effectiveness |  |
| Accuracy Over Space | Provides a spatial residual mapping within an individual model. |
| Accuracy over Time | Visualizes how predictions change over time. |
| Advanced Tuning | Allows you to manually set model parameters, overriding the DataRobot selections. |
| Anomaly Assessment | Plots data for the selected backtest and provides SHAP explanations for up to 500 anomalous points. |
| Anomaly over Time | Plots how anomalies occur across the timeline of your data. |
| Confusion Matrix | Compares actual data values with predicted data values in multiclass projects. For binary classification projects, use the confusion matrix on the ROC Curve tab. |
| Feature Fit | Removed. See Feature Effects. |
| Forecasting Accuracy | Provides a visual indicator of how well a model predicts at each forecast distance in the project’s forecast window. |
| Forecast vs Actual | Compares how different predictions behave at different forecast points to different times in the future. |
| Lift Chart | Depicts how well a model segments the target population and how capable it is of predicting the target. |
| Residuals | Clearly visualizes the predictive performance and validity of a regression model. |
| ROC Curve | Explores classification, performance, and statistics related to a selected model at any point on the probability scale. |
| Series Insights | Provides series-specific information. |
| Stability | Provides an at-a-glance summary of how well a model performs on different backtests. |
| Training Dashboard | Provides an understanding about training activity, per iteration, for Keras-based models. |
| Understand: Explains what drives a model’s predictions |  |
| Feature Effects | Visualizes the effect of changes in the value of each feature on the model’s predictions. |
| Feature Impact | Provides a high-level visualization that identifies which features are most strongly driving model decisions. |
| Cluster Insights | Captures latent features in your data, surfacing and communicating actionable insights and identifying segments in your data for further modeling. |
| Prediction Explanations | Illustrates what drives predictions on a row-by-row basis using XEMP or SHAP methodology. |
| Word Cloud | Displays the most relevant words and short phrases in word cloud format. |
| Describe: Model building information and feature details |  |
| Blueprint | Provides a graphical representation of the data preprocessing and parameter settings via blueprint. |
| Coefficients | Provides, for select models, a visual representation of the most important variables and a coefficient export capability. |
| Constraints | Forces certain XGBoost models to learn only monotonic (always increasing or always decreasing) relationships between specific features and the target. |
| Data Quality Handling Report | Provides transformation and imputation information for blueprints. |
| Eureqa Models | Provides access to model blueprints for Eureqa generalized additive models (GAM), regression models, and classification models. |
| Log | Lists operation status results. |
| Model Info | Displays model information. |
| Rating Table | Provides access to an export of the model’s complete, validated parameters. |
| Predict: Access to prediction options |  |
| Deploy | Creates a deployment and makes predictions or generates a model package. |
| Downloads | Provides export of a model binary file, validated Java Scoring Code for a model, or charts. |
| Make Predictions | Makes in-app predictions. |
| Compliance: Compiles model documentation for regulatory validation |  |
| Compliance Documentation | Generates individualized model documentation. |
| Template Builder | Allows you to create, edit, and share custom documentation templates. |
| Comments: Adds comments to a modeling project |  |
| Comments | Adds comments to items in the AI Catalog. |
| Bias and Fairness: Tests models for bias |  |
| Per-Class Bias | Identifies if a model is biased, and if so, how much and who it's biased towards or against. |
| Cross-Class Data Disparity | Depicts why a model is biased, and where in the training data it learned that bias from. |
| Cross-Class Accuracy | Measures the model's accuracy for each class segment of the protected feature. |
| Insights and more: Graphical representations of model details |  |
| Activation Maps | Visualizes areas of images that a model is using when making predictions. |
| Anomaly Detection | Lists the most anomalous rows (those with the highest scores) from the Training data. |
| Category Cloud | Visualizes relevancy of a collection of categories from summarized categorical features. |
| Hotspots | Indicates predictive performance. |
| Image Embeddings | Displays a projection of images onto a two-dimensional space defined by similarity. |
| Text Mining | Visualizes relevancy of words and short phrases. |
| Tree-based Variable Importance | Ranks the most important variables in a model. |
| Variable Effects | Illustrates the magnitude and direction of a feature's effect on a model's predictions. |
| Word Cloud | Visualizes variable keyword relevancy. |
| Learning Curves | Helps to determine whether it is worthwhile to increase dataset size. |
| Speed vs Accuracy | Illustrates the tradeoff between runtime and predictive accuracy. |
| Model Comparison | Compares selected models by varying criteria. |
| Bias vs Accuracy | Illustrates the tradeoff between predictive accuracy and fairness. |

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