Like all machine learning algorithms, Decision Forests have hyper-parameters. The default values of those parameters give good results, but, if you really want the best possible results for your model, you need to "tune" those parameters. TF-DF makes it easy to tune parameters. Starting with TF-DF 1.0, you can use the pre-configured hyper-parameter tuning search space. Check the hyper-parameter tuning tutorial for more details.

The tool, dubbed Simple ML, is available in beta. It's provided as an addon to Google Sheets that users can download at no charge. Simple ML was created by one of the Google teams responsible for developing TensorFlow, a popular open-source AI tool that the search giant released in 2015.

エンタメにおいても人工知能は日進月歩で発展しており、新しい研究成果や試みが次々と発表されています。こちらの連載

Last week, Google announced and released a beta version of Simple ML for Sheets, a TensorFlow Decision Forests-produced add-on for Google Sheets. This release is one of the first of its kind, offering many simple and some complex machine learning functionalities directly to Google Sheets users. Although Simple ML has been touted as the machine learning solution for people with no prior knowledge of machine learning, the Advanced Tasks it offers promise value to data scientists, machine learning experts and anyone else working with bigger datasets. Read on to learn more about this release and how it may shape spreadsheet-based data and machine learning projects in the future. Simple ML for Sheets is currently available in beta.

Check out all the on-demand sessions from the Intelligent Security Summit here. Spreadsheets are widely used by organizations of all sizes for all kinds of basic and complex tasks. While simple calculations and graphs have long been part of the spreadsheet experience, machine learning (ML) has not. ML is often seen as being too complex to use, while spreadsheet usage is intended to be accessible to any type of user. Google is now trying to change that paradigm for its Google Sheets online spreadsheet program.

Advancements in Machine Learning technology have the power to revolutionize various fields.

While simple calculations and graphs have long been part of the spreadsheet experience, machine learning (ML) has not.

I recently created a'particle optimizer' and published a pip python package called kernelml. The motivation for making this algorithm was to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients. The optimizer uses a combination of simple machine learning and probabilistic simulations to search for optimal parameters using a loss function, input and output matrices, and (optionally) a random sampler. I am currently working more features and hope to eventually make the project open source. Lets take the problem of clustering longitude and latitude coordinates.