ML.NET PART 4 – MACHINE LEARNING

2 weeks ago, in my part 3 post of my ML.NET adventure, i wrote about AutoML through Command Line Interface CLI and how i generated and i wish to expand more on them

What AutoML does and its coverage

As of time of writing, there are 3 that has incorporated into AutoML,

  1. binary-classification
  2. multiclass-classification
  3. regression

I also understand from Microsoft Docs that there will be future machine learning tasks that can be incorporated.

ML.NET CLI input and output
ML.NET CLI input and ouput

Image copied from https://docs.microsoft.com/en-us/dotnet/machine-learning/automate-training-with-cli

The various commands possible

> mlnet auto-train --task binary-classification --dataset "customer-feedback.tsv" --label-column-name Sentiment
> mlnet auto-train --task regression --dataset "cars.csv" --label-column-name Price
> mlnet auto-train --task multiclass-classification --dataset "Training.csv" --label-column-name "Risk" --max-exploration-time 600

Source – https://docs.microsoft.com/en-us/dotnet/machine-learning/reference/ml-net-cli-reference

Output from ML.NET

After running the respective commands for ML.NET, you will noticed 1 folder that will consists of

  1. logs folder
  2. ConsoleApp folder
  3. Model folder
  4. sln file (solution)

Logs – The logs file consists of a full logs with information on all the iterations that have happened while evaluating the algorithms.

ConsoleApp – This application, in C#, allows you to run and make predictions like an end-user applicaiton

Model –
it consists of MLModel.zip which is a serialized model that is ready to use for running predictions
it also consists of the code that was used to generate the model which we can use for retraining purposes.

Quality of the generated model

Understanding more on the quality of the model that was generated.

You will notice –
with Binary Classification – comes
1. Accuracy
2. AUC
3. AUPRC
4. F1-Score

with Multiclass Classification – comes
1. MicroAccuracy
2. MacroAccuracy

with Regression – comes
1. RSquared
2. Absolute-loss
3. Squared-loss
4. RMS-loss

You will be able to see how to understand the metrics via this link – https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics