Understanding and interpreting machine learning model decisions is crucial for transparency, accountability, and confidence in results. Also, it helps it appear less like a black box.

Many methods have been created over the years to do this, and while they all have a common goal, they all take somewhat different approaches and are best suited to slightly different models.

Here are some of the most popular methods utilized by programmers for making sense of machine learning models.

Lucid

The increasing prevalence of deep learning in several industries necessitates the urgency to provide explanations for these models. Nevertheless, this might be a significant challenge due to the extensive array of elements that must be addressed. The primary objective of the Lucid library is to address this deficiency by offering a range of resources for the visualization of neural networks. What is the most favourable aspect? Neural networks can be shown without the need for any pre-existing configuration.

Lucid is a specialized platform created by researchers and managed by volunteers. Its primary focus lies in neural networks and deep learning models. The system comprises Modelzoo, a preloaded component encompassing a range of deep-learning models.

Captum

Captum is a PyTorch library offering various interpretability algorithms, including Integrated Gradients and DeepLIFT. These methodologies facilitate users in attributing the model's predictions to certain input features and comprehending the decision-making process.

Anchors

The anchors technique is optimal for image classification models because it identifies the minimal set of image regions, denoted as anchors, from which decisions can be made. These anchors are generated by identifying regions with high-class activation values corresponding to the region where the model makes its predictions.

TensorFlow Explainability

This tool provides interpretability techniques, such as Integrated Gradients and Occlusion Sensitivity, unique to TensorFlow-based AI models. It assists users in comprehending the model's behaviour and the significance of various input features.

SHAP

SHAP is an acronym for SHapley Additive exPlanations. It uses game theory to explain the output of any machine learning model. It explains the prediction of an instance by using Shapley values to compute the contribution of each feature to that prediction.

These values represent the average marginal contribution of every feature value across all potential coalitions. Despite its complexity and difficulty for novices, it is regarded as the most intuitive method for assessing the interpretability of models. 

ELI5

ELI5 is a Python library that makes explaining and interpreting ML models relatively simple. Instead of a specific/agnostic interpretation, models are interpreted in terms of their local/global scope. It currently requires scikit-learn 0.18 or later to operate.

The library permits the explication of weights, the prediction of linear classifiers and regressors, and the printing of decision trees as text or SVGs. In addition to displaying feature weights, it explains the predictions of decision trees and tree-based ensembles. It is primarily used for examining model parameters and determining how the model operates globally or for examining individual model predictions and relating them to the model's decision-making process. Only Keras, XGBoost, LightGBM, CatBoost, lightning, and sklearn-crfsuite support the model.

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