Machine learning models are frequently seen as black boxes, i.e. one does not know exactly how an algorithm arrived at its prediction. ML models are useful for making a prediction but otherwise incomprehensible. To overcome this problem, the user must know in advance how algorithms arrived at their particular outcome. Data scientists are able to extract real value and insights from any model.
At Clappform we value transparency in our solutions. To provide in addition to the prediction, explainability about the model and analytics. We are able to answer questions for any given model, to get in-depth information, such as; what features in the data did the model think are most important? What interactions between features have the biggest effects on a model’s predictions? And how did each feature in the data affect that particular prediction?
These questions can be answers by data scientists, applied to every particular industry. In the real estate sector for instance, where machine learning models can predict future housing prices and find out which features interact, or which features are the most important to predict housing prices.
The five most important applications of model insights:
Debugging is one of the most valuable skills in data science. Because of the huge amount of unreliable, and disordered data from the internet and other sources, debugging is highly essential. Bugs can lead to frequent and potentially disastrous consequences. Understanding the patterns and outcomes of a model will help you identify when those patterns are clashing with your own real-world knowledge. This is the first important step in detecting bugs.
Informing feature engineering
Using feature engineering, it is possible to repeatedly create new features using transformations of raw data or features that have been created previously. Feature engineering is generally the most efficient way to improve model accuracy. In this process, you can go through it, using intuition or using additional directions when there isn’t enough background knowledge. Besides, it is possible to identify important features in certain models, that could create new powerful features.
Directing future data collection
Data scientists in organizations and businesses are able to expand the types of data they collect. Using more data can, on the one hand, lead to an expensive process, but on the other hand, to a better model. Data scientists experiment with if adding new types of data is worthful. Insights derived from the model offer a good understanding of the current value of features. The decision about adding new types of data is based on these current values.
And lastly, the importance of building trust. People will not assume they can trust a model to make decisions without verifying some basic facts. Or people are reluctant because of the existence of data errors. In fact, showing insight based on machine learning models that match with people’s general perception, could help to build trust among them. Nowadays we see more people relying on insights made by models and expect this to continue.
Clappform can be used for any kind of machine learning, from classic machine learning to deep learning. Our data scientists are ready to help your business in dealing with machine learning more efficiently, accurately, and effectively. We provide model insights and explainability for your business to make more informed decisions and help you improve with your targets. We believe in a low-code environment structure, where it possible for anyone to use data, models, and predictions in a friendly, understandable, and flexible way.
Do you want to learn more about machine learning and how we can help you growing your business? Please contact us.
Whatever challenges you have, we are happy to help!
Do not hesitate to contact us or request a free demo.