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Explainable AI

What-if

Enables teams to run hypothetical scenarios to understand what features need to be tweaked to obtain desired predictions

The 'What-if' feature enables teams to run hypothetical scenarios to understand what features need to be tweaked to obtain desired predictions. Such scenarios can provide quick insights into the model to the users.

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F-Score (F1-Score)
Constant Features
High Feature Correlation
Target Drift
Stochastic Gradient Descent (SGD)
RandomForest
CatBoost (Categorical Boosting)
LightGBM (Light Gradient Boosting Machine)
XGBoost (eXtreme Gradient Boosting)
CTGAN (Conditional Tabular Generative Adversarial Network)
GPT-2 (Generative Pre-trained Transformer 2)
Internet Information Service Algorithm Recommendation Management Regulations
Generative AI Measures in China
Provisions on the Administration of Deep Synthesis of Internet-based Information Services
Artificial Intelligence and Algorithmic Fairness Initiative
The EU AI Act
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Federal Trade Commission (FTC)
President Biden's Executive Order on AI
Principles for Responsible AI
Digital India Act
Draft National Data Governance Framework Policy
National Strategy for Artificial Intelligence #AIFORALL: NITI Aayog
National Cybersecurity Reference Framework
Global Partnership on Artificial Intelligence (GPAI)
Top-k
Temperature
Low-Rank Adaptation (LoRA)
Quantization
Hallucination
Multi-modal models
Mixture of experts (MoEs)
Mamba
Opensource vs. Closed Source Models
Tokens
Diffusion Models
Transformers Models
Model safety
Vector database
Large Language Models (LLMs)
Foundation models
Synthetic AI
Generative AI
Z-Test
Chi-square test
Kolmogorov–Smirnov test (K–S test or KS test)
Wasserstein distance
Jensen-Shannon(JS) Divergence
Population Stability Index (PSI)
Kullback-Leibler (KL) divergence
ML Audit
Bias Monitoring
Data Monitoring
ML Observability
MLOps
Regression model
Classification model
Model confidence score
Model threshold
Surrogate models
What-if
Similar Cases
Feature Importance Store
LOCO
SHAP
LIME
Explainability
Fairness/Bias Monitoring
ML Monitoring
Accuracy
Data Processing
Precision
Recall/ Sensitivity or True Positive Rate
Specificity/ True Negative Rate:
Precision-recall curve
Confusion Matrix
F score
ROC Curves and ROC AUC
Data Drift
Model Drift

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