> For the complete documentation index, see [llms.txt](https://subramanian-pr.gitbook.io/research-paper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://subramanian-pr.gitbook.io/research-paper/3.-interpretable-machine-learning/3.2-method.md).

# 3.2 Methods

We have methods listed below to proceed. Below are the methods, that diverge under the Interpretable Machine Learning

* **Machine Learning Models**
* **Model-Agnostic**
* **Sample Theory**

**Machine Learning Models** - will be helping us with a specific algorithm to build the interpretable models and start working with it, in addition to it to help us explain the working

**Model-Agnostic** - Separating the explanations from the machine learning model (= model-agnostic interpretation methods) has some advantages, and we are going to discuss it in the coming sections.

**Sample Theory** - Is not related to the model interpretability, but a research sub-topic that would help us increase the computational speed to achieve our goal for interpretation and dealing with huge data to build the interpretable models. This is just a supporting method for our book, and should not be considered important for an Evaluation System.