User Guide

Here you can find the key concepts, structure, recommended flow, and dive in to many of the deepchecks functionalities.

General

General

• Deepchecks Hierarchy
  • Check
  • Condition
  • Suite
• Viewing Deepchecks Results
  • Main Methods for Showing Results
  • Alternative Methods for Showing Results (Troubleshooting)
• Exporting Deepchecks’ Results
  • Save Result as an HTML Report: save_as_html()
  • Export Results to JSON: to_json()
• Customizations
• Metrics Guide
  • Default Metrics
  • Alternative Metrics
  • List of Supported Strings
  • Custom Metrics
• Drift User Guide
  • What Is Distribution Drift?
  • Which Types of Drift Are There?
  • How Do You Detect Drift?
  • How Can I Use Deepchecks to Detect Drift?
  • What Can You Do in Case of Drift?
  • Code Examples

Tabular

Tabular

• Tabular Quickstarts
• Tabular Tutorials
• The Dataset Object
  • Class Properties
  • Creating a Dataset
  • Useful Functions
  • Inferring Categorical Features
• Working with Models and Predictions
  • Supported Tasks and Predictions Format
  • Passing a Model
  • Using Pre-computed Predictions
• Feature Importance
  • What is Feature Importance?
  • Why Does Deepchecks Use Feature Importance?
  • How Does Deepchecks Get Feature Importance?
  • What if the Feature Importance Calculation Takes Too Long?
• Custom Check Templates
  • Single Dataset Check
  • Train Test Check
  • Model Only Check

Vision

Vision

• Computer Vision Quickstarts
• Computer Vision Tutorials
• Data Classes
  • The Vision Data Class
  • The Classification Data Class
  • The Object Detection Data Class
  • Why Is This Important?
  • What Do You Need to Implement?
  • Validating Your Data Class Implementation
• Data Properties
  • What Are Properties Used For?
  • Deepchecks’ Built-in Properties
  • Property Structure
  • Properties Demonstration
• Custom Check Templates
  • Single Dataset Check
  • Train Test Check
  • Model Only Check

Integrations

Integrations

• Spark & Databricks
  • Loading the dataset
  • Defining deepchecks Dataset
  • Running the Integrity Suite
  • Building a Logistic Regression Model
  • Evaluating the Model Using Deepchecks Suites
• Pytest
  • Defining Pytest Fixtures
  • Implementing the Test
• H2O
• HuggingFace Transformers
  • Implement a DetectionData Class for the DETR Model
  • Load COCO and YOLOv5s
  • Benchmarking YOLOv5s Against DETR ResNet
• Airflow
  • Defining the Data & Model Loading Tasks
  • Defining the Integrity Report Task
  • Defining the Model Evaluation Task
  • Creating the DAG
• Weights & Biases (wandb)
  • Load Data
  • Run a Check
  • Observe CheckResult Display and Value
  • See Check to_wandb options
  • Run Suite and export to wandb

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When Should You Validate & Built-In Suites

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Deepchecks Hierarchy