Month: October 2021

How To Make Sure Your Machine Learning Models Aren’t…

One of the truisms of machine learning models is that sometimes, not even the model creator knows what is going on inside the model. It is one of the many reasons why explainable AI is now such a big focal point in the industry.

Many people see the fact that machine learning models should not be black boxes. It causes problems in a world where machine learning algorithms are being utilized to make a lot of decisions that have a real impact on how people live their lives. An ML model needs to be trained on data. However, the end result isn’t always understood completely by the team that created the model. It means you might have a model in production that no one understands how it works on the inside.

Putting Model Interpretability Into Words

The point of machine learning algorithms is to make accurate predictions based on a model. However, let’s say your machine learning algorithm makes a decision about a user’s credit rating. It denied them aloan that they desperately needed to keep a business running. How do you justify that decision to them? Could you explain what process the machine learning algorithm used to get to that decision? That is whatmodel interpretability is all about. It is about explainable AI and having an ML model that you can explain its decisions. Machine learning will be a bigger part of how we live our lives, which is why it is crucial that model developers be able to explain how AI reaches its decisions.

The What and How of Model Interpretability

Model training is something of an art and a science. Because of that, it might be difficult to figure out how the algorithm got to its decision. It is difficult putting the logic of an AI algorithm into words, which is what makes model interpretability so difficult. The AI algorithm is crunching the numbers on the data we put in, but we don’t know what exactly it crunches.

There might be other problems with implicit bias. For example, if you are training your ML model on faces, you might be biasing it towards one race or gender. Problems like this might crop up in the real world, where your machine learning models fail to perform adequately for one group of people. That will inevitably lead to claims about you being biased, which could potentially ruin your reputation. It is why transparency is such an important factor when developing machine learning models. Explainable AI is how you get to that level of transparency that people will be happy with.

Why Does Model Interpretability Matter?

Transparency will be key in a future where machine learning models are used in making important decisions that affect people’s lives. Because of this, it is critical that machine learning models don’t be black boxes. We will start seeing more machine learning algorithms in the medical field as well as finance.

The decisions these algorithms make could have profound impacts on the personal lives of many people. Explainable AI is crucial to building trust. People will not be willing to put their lives on the line if the ML model they depend on cannot be understood by everyone. The future of the industry will be shaped by how well machine learning models will fit into that model interpretability framework.

More Data Is Better In Machine Learning Model Creation

Data is at the heart of every machine learning model. Without it, model accuracy drops precipitously. However, there are many machine learning models that need to be developed without having as much data as the engineer would like. This is where data augmentation comes in. It is one of the primary ways of achieving model accuracy in production. However, it is quite a difficult practice to do, as it involves creating synthetic information to feed to your model.

There are not many frameworks currently available for this, which means that most engineers have to develop a solution from scratch. So, what is data augmentation?

What Is Data Augmentation

You can think of data augmentation as a process of creating synthetic data to feed into your machine learning models. For example, let’s say you are training an image recognition algorithm on a data set featuring cars. However, you might not have the necessary pictures of cars you need to feed that data set. Your model accuracy would drop quite low if you tried to put a model into production after training it on that data set.

A way you can alleviate that is to generate more pictures from the ones you already have. For example, you could have pictures of the same cars but different colors. You can also change their orientation by performing basic image manipulation algorithms on them. Doing simple things like this is what is called data augmentation.

How to Solve Data Augmentation Problems

One of the many use cases of data augmentation is when you are working on image classification algorithms. These algorithms need massive data sets to work well, but you might not have that on hand. Data augmentation solves this problem by easily helping you generate multiple versions of the same picture.

You might also be working with other classification algorithms that don’t have the necessary data set to perform adequately. It is always useful to have these tools on hand to help you do a better job with data augmentation. Sometimes it can be as simple as placing objects that shouldn’t be there in your machine learning model. The biggest benefit here is to model accuracy.

When Should You Use Data Augmentation In Your Machine Learning Process

There are a few major use cases for data augmentation in developing your machine learning models. When model accuracy is the most important thing, it should be one of the first methods you move towards to solve those problems. The next use case is when you have a small data set that needs a bit more to make your model as accurate as possible. By expanding your data set, you ensure that your AI algorithm has more to go on when training.

Finally, one of the other important areas is when you don’t have control over the input data. Real-world data might not be as clean and pristine as what you are working with in the lab to train your model. You can use data augmentation to create the rough data that your machine learning algorithm will see in production.