How to Improve the Random Forest Model

If you are searching for ways to predict something, this is the right article for you as we are going to discuss a random forest classifier and solve a very specific problem – predict a Math exam outcome. The chosen method is pretty straightforward and you will for sure get the logic behind it. After you read the article, you will be able to use the method to solve similar problems or the more advanced ones.

Random forest is a popular classification and regression method, but there are more of them in the machine learning toolkit. You can always refer to this AI blog to learn more about the full range of artificial intelligence methods and technics.

Let’s go through the article to learn the essentials of the classifier and take a closer look at the Math exam problem!

The best feature of the random forest classifier is its capability to track the connections and relationships among the predictors. And we are talking about a big number of connections, which are not interpretable by just looking at the visualization.

To improve your model, you can take a look at the importance of the features. Each feature obtains the score, and you can even visualize the scores and perform some exploratory analysis. The exploratory analysis will help you define the subsets of the strongest features and the weakest ones.

Alt: important features visualization

Once you have the subsets, try to eliminate the weakest features, and check how your accuracy changes! However, be cautious, and always check the accuracy after you eliminate even the weakest features. It is not wise to eliminate them all without checking the model’s performance, as long as it may lead to a significant drop in accuracy score.

If you want to go further with tuning, check out these hyperparameters tuning videos to get a sense of the routine.

Hyperparameter Tuning of Machine Learning Model in Python

More Random Forest Application Cases for Real-Life Problems

1. Business Data

Sales prediction tasks are prioritized by many. The algorithm helps predict online buying behavior and other phenomena by learning from the historical data possessed by private companies. This data can go from the analytics systems, customer relationship systems, offline sales points, and more.

Imagine you work at IKEA and have volumes of price data. Watch another good video example on how you can apply random forest to it.

Get started with random forest tuning and tidymodels using IKEA price data

1. Environmental Data

Environmental studies are famous for the amount of data available for researchers, so the whole industry cannot go anywhere without ML. Take a look at how the algorithm is used to research how the environment affects health.

1. Sports Analytics

Probably one of the most hyped topics of the 21st century. If you are into NBA, you will enjoy this project with the use of the random forest. Don’t hesitate to repeat it with the fresher data!

Conclusion

You have just become familiarized with the typical classification task and the approach to building predictions based on classification and with the use of the supervised machine learning method. The random forest classifier algorithm is easy to understand and implement. You can start from the obvious daily problems and move to global solutions once you drill the basics.

If you’ve made it to this sentence, we are happy to see that the article has been helpful for you. Follow the link to discuss your own project or request the ML development from an expert team, and don’t hesitate to ask for advice, on how to set your own solution on real-life data!

Disclaimer: The information on this website is provided for general informational purposes only. We make no warranties regarding the accuracy, completeness, or reliability of any content. For more such intersting content follow us @ Biz grows