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Machine Learning is all about selecting the right Machine Learning Model. There is nothing like a “good” machine learning model. Choosing a machine learning model totally depends on the dataset that you’re working on. Data scientists have come up with thousands of techniques in which you can model your dataset. This provides developers with a large pool of choices of data models to choose from. Two important factors that developers consider while selecting a machine learning model are how explainable and interpretable a model is. In this article, let’s take a deep dive into this whole talk about the explainability and interpretability of Machine Learning models.
While there are thousands of machine learning models to choose from, data scientists are always on the lookout for models that are highly explainable. Since there are a lot of assumptions and exceptions involved while dealing with huge chunks of data, it is critical for data scientists to choose a machine-learning model that is highly explainable. What exactly does it mean for a machine learning model to be explainable? Well, an explainable machine learning model is one whose predictions or decisions can be easily understood and articulated by humans. It involves providing insights into how the model arrives at a particular output, making the decision-making process transparent and interpretable. Additionally, in tasks like vector search, where finding similarity or relatedness between data points is crucial, having an explainable model can be immensely beneficial in comprehending the rationale behind the retrieved results.
What good is a machine learning model that does not even help you in interpreting the results that you get out of the computations and calculations? Machine learning models that are easily interpretable, help data scientists reach conclusions and quantify their research. Such models are considered interpretable machine learning models. In exact words, interpretable machine learning models are models whose predictions or decisions can be easily understood and explained by humans. Interpretable models allow users to grasp the relationships between input features and the model’s output, providing insights into how the model is making decisions.
Now how do data scientists simply know which machine learning model is explainable and interpretable enough for modeling the dataset that they are dealing with? There are certain characteristics of a data model that data scientists consider before selecting a particular data model. Let’s have a look at those key characteristics of the machine learning models that make them explainable and interpretable among all others.
Very obviously, you can’t understand something you can’t observe. You need to be able to look at how data is being processed, what computations are being done on the data, how is data being mapped, and other similar stuff, so that you can reach a conclusion. Machine Learning is all about observing details and catering to them. Until you don’t have a transparent modeling system in place, you can’t reach fruitful conclusions for your dataset.
The model’s output is presented in a way that is easily comprehensible to both technical experts and non-experts. This may involve using simple language, visualizations, or other means to convey complex concepts. The predictions and decisions made by the model must be so that can be easily understood and articulated by humans.
It is crucial for machine learning models to be consistent throughout the results that they provide. The model’s behavior should be consistent such that any changes in the input data or features should result in corresponding changes in predictions. Consistency is crucial for building trust in the model. Only when a machine learning model is consistent, can you actually believe the predictions and the decisions that it comes up with.
The decision-making process of the model must be transparent. The decision logic also must follow a clear flow that is easy to follow. This can involve rules, thresholds, or other straightforward mechanisms.
Often neglected, a good user-friendly interface is yet another key characteristic that makes a machine-learning model interpretable. Imagine you having such a hard time understanding the interface of a machine learning model that you can’t understand what goes on exactly on the dataset. This is exactly why a machine learning model with a user-friendly interface is highly important.
Why are we breaking our heads over this whole concept of explainability and Interpretability of Machine learning models so much? There are many reasons why it is necessary and important for a machine learning model to be explainable and/or interpretable. We’ll list those reasons here.
Will you use the same machine learning model over and over again if you’re not sure whether it’s producing the right results for you? Probably not. But in a scenario, where you are already aware of how easily explainable and interpretable a machine learning model is for the dataset that you provide it with, you are likely to use that again and again. Explainability and Interpretability give you that trust and belief in a machine learning model and you don’t have to go search for the right machine learning model from zero for every other dataset you work on.
In many industries, there are legal and ethical requirements for transparency in decision-making processes. Explainability is crucial for compliance with regulations and standards.
Users, whether they are domain experts or end-users, need to understand and trust the decisions made by machine learning models. Interpretability provides insights into the model’s inner workings and makes the decision-making process more accessible.