Scala Machine Learning Projects

Typical machine learning workflow

A typical ML application involves several processing steps, from the input to the output, forming a scientific workflow as shown in Figure 1, ML workflow. The following steps are involved in a typical ML application:

1. Load the data
2. Parse the data into the input format for the algorithm
3. Pre-process the data and handle the missing values
4. Split the data into three sets, for training, testing, and validation (train set and validation set respectively) and one for testing the model (test dataset)
5. Run the algorithm to build and train your ML model
6. Make predictions with the training data and observe the results
7. Test and evaluate the model with the test data or alternatively validate the model using some cross-validator ...