Path3.Mod1.b - Automated Machine Learning - Featurization and Models

Question 1

Differences between Training Data, Validation Data and Test Data

Answer 1

• Training Data: Used to train your models
• Validation Data: Used to tune hyperparameters.
• Test Data: AutoML’s recommended model is tested at the end with this data if provided (preview only)

Question 2

The problem with Validation Data in AutoML

Answer 2

AutoML uses the same data per iteration of Hyperparameter tuning, creating a model evaluation bias as the model continues to improve and fit to the Validation Data.

Question 3

Define:
- Feature Engineering
- Featurization wrt Feature Engineering

Answer 3

• Using domain knowledge to create features to help ML learn better.
• The collective techniques of scaling and normalization coupled with Feature Engineering.

Question 4

Define Customizing Featurization and how to enable it

Answer 4

Additional feature engineering techniques (data manipulation) such as encoding and transforms. These can be enabled through ML Studio and the Python SDK

Question 5

Define Ensemble Models

Answer 5

The combination of multiple models to improve machine learning results and predictive performance (ensemble learning).

Question 6

V WA, St heter

The two types of Ensemble Model Combining Methods and which uses a Meta-Model

Answer 6

• Voting: predicts on weighted average of either predicted class probabilities (classification) or regression targets.
• Stacking: combines heterogenous models and trains a meta-model based on the output from the individual models.

Question 7

LR EN

The two default Ensemble Meta-Models (and for which type of ML task)

Answer 7

• LogisticRegression: for Classfication tasks
• ElasticNet: for Regression and Forcasting tasks

Question 8

Define ONNX Format and its usage

Answer 8

Open Neural Network Exchange, an open format built for representing ML Models. You can convert your models to ONNX format to run on a variety of platforms

https://onnx.ai/

Question 9

The ONNX Runtime does not support C#, only Python based models. Ergo you cannot run models in C# applications (T/F).

Answer 9

False. The ONNX Runtime also supports C#, so you can use ONNX formatted models in your C# apps

https://onnx.ai/