Today’s businesses are harnessing the power of machine learning (ML) more than ever. According to a report, the ML market size was around USD$15.44 billion in 2021. This statistic is projected to expand from USD$21.17 billion (2022) to USD$209.91 billion (2029) at a compound annual growth rate of 38.8%.
This growth is due to advancements in artificial intelligence (AI), which has enabled organizations to automate various processes. Ensemble learning is one of the ML models that’s now getting much attention.
What Is Ensemble Learning?
Ensemble learning combines multiple ML models that outperform individual models. This technique improves the accuracy of the predictions made by the ML model. The ensemble learning process aims to create a more robust and accurate model than a single model. In other words, it reduces the error rate.
There are various ensemble learning algorithms, such as:
- Boosting: This process is sequential where each subsequent model tries to correct the errors of the previous model.
- Bagging: This parallel process involves training each model in the ensemble independently on a random subset of the data.
- Stacking: This process is a combination of boosting and bagging. It’s a two-level ensemble learning method where the output of each model is used as an input to train the next model.
Now that you know the basics of ensemble learning, let’s look at some industries that benefit the most from this technology.
- Healthcare Industry
The healthcare industry is one of the top beneficiaries of ensemble learning. This is because the healthcare sector deals with a lot of data, which medical practitioners need to analyse to provide better patient care. One example is medical images, such as ultrasound, magnetic resonance imaging, X-rays, and more. According to research, medical images make up around 90% of all healthcare data, and more than 95% of these data are unstructured. Healthcare professionals need to interpret these images to make an accurate diagnosis. Otherwise, the wrong treatment could be given to patients, leading to severe consequences. This is where ensemble learning can help.
Ensemble learning can automatically classify medical images and identify problems. By training different models on the same dataset and combining their predictions, the accuracy of the results has increased. This method is beneficial in detecting rare diseases as there are usually not enough data to train a single ML model. This, in turn, could help save lives as early detection is key to a personalised and successful treatment plan.
- Retail Industry
Did you know that the retail industry is one of the most significant users of ML? According to a survey, around 45% of retail companies deploy ML for customer engagement, 41% for supply chain logistics and management, and 37% for demand forecasting. With these vast applications, retailers may upgrade the use of predictive analytics to enhance the customer experience, recommend products, and optimise stock levels. Ensemble learning will play a significant role in these applications.
Ensemble learning is particularly well suited for the retail industry because of the large and complex datasets. For example, a retail company like yours may have customer purchase history, product inventory, and store sales data. These data sets can be combined to develop predictive models that you can use to make decisions about pricing, promotions, and product assortment. As a result, your retail business can become more customer-centric and agile in the face of market changes.
- Banking And Finance Businesses
It’s no secret that the banking and finance industry is heavily regulated. Financial institutions must comply with anti-money laundering regulations and know your customer guidelines.
Ensemble learning can be a valuable tool for banks and other financial businesses to meet these compliance requirements by detecting patterns of fraudulent behaviour. You can train the algorithms on historical data to see unusual or suspicious activities, such as large cash withdrawals or transfers to accounts in known tax havens.
You can also use ensemble learning to develop predictive models for financial market analysis. For instance, you can train this technology to identify patterns in stock prices or currency movements. You can take advantage of these predictions about future market conditions. Consequently, ensemble learning can give your financial business a competitive edge in the industry.
- Manufacturing Companies
There are many moving parts in a manufacturing process, from raw materials to finished products, and each area needs to be monitored and controlled to ensure quality and efficiency. However, with so many data points to track, it can be challenging to get a clear picture of the manufacturing process.
Ensemble learning can develop predictive models to help manufacturers optimise their production processes. For example, algorithms can be trained to identify patterns in machine data, such as temperature, vibration, and pressure readings. You can then use these models to predict when a machine is likely to break down or need maintenance. More importantly, you can use the models to check product quality proactively. As a result, your manufacturing business can become more efficient and reduce the risk of product defects.
Conclusion
These are just some of the industries that can benefit from ensemble learning. With its ability to handle large and complex datasets, this technique can be used in several applications to improve predictions and decision-making. So, if you’re working with data, it’s worth considering ensemble learning as a tool to help you achieve your business goals.
