Everything you need to know about machine learning: part 2
- select the contributor at the end of the page -
Hands-on exampleMicrosoft has provided an online Web-based interface to work with MAML, named Azure Machine Learning Studio. I’m not going to get into the finer details of Azure ML Studio detailing, like how to drag and drop and link things together, but in Part 3 of this series, you'll get a video that walks you through how I’ve assembled all of the modules or blocks together. If you need additional guidance, head here and enter “Azure Machine Learning” in the search box, and additional information will be available. Using the Kaggle Titanic data is an example of a supervised machine learning scenario. Basically, I have data that I will define having a label and features. In this case, the features I'll use to create my model are “passenger class,” “sex,” “age” and “fare”, and I'll define the label as whether the particular person survived or not, which is defined by “survived.” This is what the workflow for this example can look like in MAML Studio: If we go block-by-block starting from the top, briefly:
- Add our data set.
- Select certain features (because such things as the name may not be relevant to training a model).
- Clean our data (some passengers had an empty age, so I filled in empty values with the median age).
- Spit the data (I chose to use 80 percent of my data to train my model, and the remaining 20 percent to test).
- Setup the model with 80 percent of the original data and initialize it as a “Multiclass Decision Forest,” which is a classification-type algorithm.
- Score the model.
- Evaluate the model.