#ml monitoring

Interpretability ML is a field of study that aims to make machine learning algorithms more interpretable. Machine learning models are powerful tools for helping us make sense of data, but they can be difficult for humans to understand because they often output numbers that don’t mean anything to us. Interpretability ML is about making these algorithms easier for people to understand so that we can make better decisions based on them.

Interpretability ML has two main goals:

Providing explanations for how a model works so that users can understand the reasoning behind its predictions.

Making it possible to optimize a model’s parameters based on human feedback.

Interpretability is an important aspect in ML monitoring as well since it helps us detect any possible issues in our models or even find out if they are working correctly or not.

Why is machine learning interpretability important?

Machine learning models are often used to make decisions that affect people's lives, such as predicting whether a person will default on a loan or commit a crime. It is therefore important that these decisions are made in an unbiased way.

One way of identifying bias in machine learning models is by training the model on labeled data sets with many different examples of each type of label you want to predict (such as whether an individual will default). The more labels in your dataset, the more confident you can be that your model doesn't have any bias. However, this approach doesn't always work because some biases aren't easy for humans to identify – for example, if someone has darker skin than others who have similar credit scores as them?

Therefore we need another method: interpretable machine learning models! These allow us access into how our model works so we can understand why it made certain predictions about individuals' future behavior based on their past actions - which could help us improve upon it in future iterations or even find evidence of its own inherent biases!

When should you use interpretable machine learning?

You may find yourself asking, "When should I use interpretable machine learning?" Here are some situations where it makes sense:

You want to understand the behavior of your model. If you're building a model and want to understand why it came up with a certain result, interpretability can be helpful in understanding this logic.

You want to be able to explain the logic of your model. Perhaps you want someone else (like an investor) or perhaps yourself at another time (after forgotten details), so that they can understand how the model comes up with its predictions.

You want to be able to communicate the logic of your model and how it relates back to training data points (regardless of whether these data points were used directly by any part of your model). In other words, if someone asked you what features were being used by some section of code that is acting as an interpreter for another section of code that generates predictions from new data points based on existing training data sets then having access would allow them quickly identify those relevant features without having access their own instance running locally inside their own computer without internet connection but rather just by looking at file system structure somewhere else entirely

How is interpretability connected to machine learning?

When it comes to machine learning, interpretability is all about understanding the data and understanding how a model makes predictions. As a result, interpretability can help us understand the model itself, what it learns from training data, why it makes certain predictions or doesn't make others—all useful information when making decisions based on machine learning models.

This isn't just relevant for academics who want to better understand how these models work: business users also need this kind of insight in order to make good decisions with their data.