> ## Documentation Index
> Fetch the complete documentation index at: https://landinglens.docs.landing.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Analyze Model Performance
export const vp = 'Visual Prompting';
export const smartLabel = 'Smart Labeling';
export const productLL = 'LandingLens';
export const mi = 'Mobile Inference';
export const llsf = 'LandingLens on Snowflake';
export const companyName = 'LandingAI';
*This article applies to these versions of LandingLens:*
LandingLens
LandingLens on Snowflake
✓
✓
LandingLens offers several tools to help you analyze and understand model performance, including: F1 scores, Precision scores, Recall scores, confusion matrices, and more. Plus, you can view this data at the model level, class level, and evaluation set level.
Use these tools and more to help you improve and iterate on computer vision models in LandingLens!
Confidence thresholds are only applicable to Object Detection and Segmentation projects.For **Object Detection**, the F1 score combines precision and recall into a single score, creating a unified measure that assesses the model’s effectiveness in minimizing false positives and false negatives. For **Classification**, the F1, Precision, and Recall scores are identical. This is because Classification models have only two prediction outcomes: "Correct" and "Misclassified".