The latest BigML release brings a tried and true Machine Learning algorithm to the platform: Linear Regression. We intend to make it generally available on Thursday, March 21, 2019. This simple technique is well understood and widely used across industries. As such, it has been a frequently requested algorithm by our customers and we are happy to add it to our collection of supervised learning methods.
As the name implies, this algorithm assumes a linear relationship between the input fields and the output (objective) field, which enables you to discover relationships between quantitative, continuous variables. Since BigML has advanced data transformation capabilities, our implementation of linear regression can support any type of field including categorical, text, and items fields, and can even handle missing values. To give a sense of how Linear Regression is applied out in the real world, it’s often used to analyze product performance, conduct market research, perform sales forecasting, and make stock market predictions, among many other use cases.
One of the main benefits of Linear Regression is its simplicity, which affords a high level of interpretability. This makes it a good technique for doing quick tests and model iterations to establish a baseline to solve regression problems. Like any other technique, there are tradeoffs so there will be circumstances where Linear Regression is not a suitable model for your uses case. We will explain some of those considerations in more detail in our subsequent posts.
As usual, this release comes with a series of blog posts that progressively explain Linear Regression through a real use case and brief tutorials on how to apply it via the BigML Dashboard, API, WhizzML and bindings. While we will not be having a live webinar for this release, feel free to contact us at firstname.lastname@example.org with any questions or feedback as always.
Want to know more about Linear Regression?
If you are curious to learn more about how to apply Linear Regression using the BigML platform, please stay tuned for the rest of this series of blogs posts to be published over the next week.