AB#1037347: Select and customize architectures and hyperparameters using random forest

Author: ShawnKupfer

learn-pr/azure/machine-learning-architectures-and-hyperparameters/1-introduction.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Introduction
   description: Introduction to the introduction to regression module.
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit

learn-pr/azure/machine-learning-architectures-and-hyperparameters/2-decision-trees.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Decision trees and model architecture
   description: Conceptual unit about decision trees and model architecture
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit

learn-pr/azure/machine-learning-architectures-and-hyperparameters/3-exercise-decision-trees.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Exercise - Decision trees and model architecture
   description: Learn how to build a simple model.
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit

learn-pr/azure/machine-learning-architectures-and-hyperparameters/4-random-forests.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Random forests and selecting architectures
   description: Conceptual unit about random forests and selecting architectures
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit

learn-pr/azure/machine-learning-architectures-and-hyperparameters/5-exercise-random-forests.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Exercise - Selecting random forest architectures
   description: Exercise about selecting random forest architectures
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit

learn-pr/azure/machine-learning-architectures-and-hyperparameters/6-hyperparameters.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Hyperparameters in classification
   description: Conceptual unit about hyperparameters in classification
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit

learn-pr/azure/machine-learning-architectures-and-hyperparameters/7-exercise-hyperparameters-tuning.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Exercise - Hyperparameter tuning with random forests
   description: Exercise about hyperparameter tuning with random forests
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit

learn-pr/azure/machine-learning-architectures-and-hyperparameters/8-knowledge-check.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Knowledge check
   description: Multiple-choice questions
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit
@@ -27,7 +27,7 @@ quiz:
       explanation: "Correct."
   - content: 'What does selecting model architectures mean?'
     choices:
-    - content: "Picking a model’s parameter values before training"
+    - content: "Picking a model's parameter values before training"
       isCorrect: false
       explanation: "Incorrect."
     - content: "Deciding how a model is structured"

learn-pr/azure/machine-learning-architectures-and-hyperparameters/9-summary.yml

@@ -6,7 +6,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Summary
   description: An overview of the content covered in the module.
-  ms.date: 07/21/2024
+  ms.date: 03/24/2025
   author: s-polly
   ms.author: scottpolly
   ms.topic: unit

learn-pr/azure/machine-learning-architectures-and-hyperparameters/includes/1-introduction.md

@@ -1,10 +1,10 @@
-Not all models are simple mathematical equations that can be plotted as a line. Instead, some complex models are easier thought of more like flow charts or traditional programming structures. Such models usually have extra levels of customization available, which can make them more powerful, though also trickier to work with. Throughout these exercises, we'll explore this by manipulating how models work and are trained. Although we'll focus on one type of model, the general principles taught here apply to many other model types as well.
+Not all models are simple mathematical equations that can be plotted as a line. Instead, it's easier to think of some complex models as more like flow charts or traditional programming structures. Such models usually have extra levels of customization available, which can make them more powerful, but also trickier to work with. Throughout these exercises, we'll explore this concept by manipulating how models work and are trained. Although we'll focus on one type of model, the general principles we cover here apply to many other model types as well.
 
 ## Scenario: Predicting sports results using machine learning
 
-Throughout this module, we'll refer to the following example scenario as we explain concepts surrounding model architecture and hyperparameters. This scenario is designed to appear complex at first, but as the exercises progress we'll learn how you can tackle it using a little critical thinking and experimentation.
+Throughout this module, we'll refer to the following example scenario as we explain concepts surrounding model architecture and hyperparameters. This scenario is designed to appear complex at first, but as the exercises progress, we'll learn how you can tackle it using a little critical thinking and experimentation.
 
-The Games' motto consists of three Latin words: Citius - Altius - Fortius. These words mean Faster - Higher - Stronger. Since this motto was established, the variety of games has grown enormously to include shooting, sailing, and team sports. We'd like to explore the role that basic physical features still play in predicting who wins a medal at one of the most prestigious sporting events on the planet. To this end, we'll explore rhythmic gymnastics: a modern addition to the games that combines dance, gymnastics, and calisthenics. One might expect that basic characteristics of age, height, and weight play only a limited role, given the need for agility, flexibility, dexterity, and coordination. Let's use some more advanced machine learning models to see how critical these basic factors really are.
+The Olympic Games' motto consists of three Latin words: *Citius, Altius, Fortius*. These words mean *Faster, Higher, Stronger*. Since this motto was established, the variety of games has grown enormously to include shooting, sailing, and team sports. We'd like to explore the role that basic physical features still play in predicting who wins a medal at one of the most prestigious sporting events on the planet. To this end, we'll explore rhythmic gymnastics: a modern addition to the games that combines dance, gymnastics, and calisthenics. One might expect that basic characteristics of age, height, and weight play only a limited role, given the need for agility, flexibility, dexterity, and coordination. Let's use some more advanced machine learning models to see how critical these basic factors really are.
 
 ## Prerequisites