Data is said to be crucial in today’s highly modern world. All forms of data are significant in forming sets that are used and processed by machines that make artificial intelligence (AI) possible.
It’s said that the way people and device systems interact with data is changing rapidly. Electronic gadgets are now capable of executing decisions thanks to the availability of a wide range of data – the process is called machine learning (ML). Devices use ML models to make them smarter and more versatile in decision making. Experts believe that the performance of an AI-capable device depends on the ML model it’s based on.
As machine learning models become more sophisticated, it’s important to remember that not all are created equal. Some models are far more trustworthy than others.
Tips to keep in mind when building a reliable ML Model
1. Consider How The Model Will Be Used
When building any machine learning (ML) model, it’s essential to consider how the model will be used. A model may be accurate and unbiased, but if it isn’t used correctly, it can still cause harm.
For example, an ML model designed to predict creditworthiness may be biased against certain groups of people if it isn’t correctly validated. Similarly, a model used to approve or deny loans automatically could also be biased if it isn’t thoroughly tested. Therefore, it’s essential to consider how the ML model will be used before deploying it.
In addition to considering how the ML model will be used, it’s also important to think about AI governance. This refers to ensuring that AI systems are ethically sound and compliant with laws and regulations.
With the rapid development of ML and AI technologies, there has been an increased focus on governance. This is because as AI systems become more complicated and powerful, they have the potential to impact more aspects of people’s lives. For example, autonomous vehicles are becoming increasingly common, and as such, there’s a need to ensure that these tech advancements are safe and reliable.
2. Collect Your Data In A Way That Is Representative Of The Real World
While building any model, the data used to train the model must represent the real world. This is because a model’s purpose is to generalize from the data it has seen and make predictions on unseen data. If the training data doesn’t represent the real world, then the model will not be able to generalize well and will not perform well on unseen data.
There are many ways to collect data in a way that isn’t representative of the real world. For example, if a dataset only contains data from a specific geographic region with completely different cultures, demographics, history, and the like, it’ll not represent the global population.
Therefore, you must ensure that data is collected to represent the real world to build an effective machine learning model.
3. Ensure That The Quality Of Data Is Clean And Free Of Errors
ML has made the world a better place to live in many ways. It changed the world for the better, and one way it does it is through its ability to help users make better decisions by analyzing large amounts of data. Nonetheless, ML is only as perfect as the data feeding it. If the data is inaccurate or full of errors, the ML model will be too.
Hence, ensuring that the data used to train the ML models is clean and free of errors is essential. Data can be cleaned in several ways, such as removing outliers or imputing missing values.
In addition to ensuring that the data is clean, it’s also essential to verify that it is accurate. This can be achieved by checking for inconsistencies and verifying sources. By taking these steps, you can ensure that your machine learning model is only as good as the data it is based on.
4. Choose An Appropriate Machine Learning Algorithm
There are many different machine learning algorithms, each with its strengths and weaknesses. Consequently, it’s essential to choose an appropriate algorithm for the task.
Case in point, if you’re building a model to classify images, you’d need to use a different algorithm than if you were building a model to predict the price of a stock.
Plus, it’s non-negotiable to consider the trade-offs between different algorithms. For instance, some algorithms are more accurate but require more data, while others are less accurate but can be trained on fewer data.
5. Go For A Simple Model
If you want to create and maintain an effective ML model, you should go for a simpler model. More specifically, you want to avoid using too many features, which can lead to overfitting. Overfitting hampers the maximum performance of a specific ML.
You want to ensure that your machine learning model is also understandable and interpretable. If the model you’ve produced isn’t understandable, it may be challenging to trust and work with.
Final Word
The abovementioned are just a few vital tips on building trustworthy ML models. By following them, you can ensure that your machine learning model is effective and reliable. Yet remember that building an effective machine learning model is a complex process, and the insights mentioned above are just a starting point. If you want to learn more about how ML and AI can benefit you, consider taking an online course or reading a book on the subject.
Check out: Machine Learning Is Effecting the Future Of Software Testing
Source@techsaa: Read more at: Technology Week Blog
