COURSE 2: MICROSOFT AZURE MACHINE LEARNING FOR DATA SCIENTIST

Module 3: Create A Classification Model With Azure Ai

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TABLE OF CONTENT

INTRODUCTION – Create A Classification Model With Azure Ai

Classification is a supervised machine learning technique used to predict categorical outcomes or class labels based on input data. In this module, you will learn to create classification models using the Azure Machine Learning designer, a tool that simplifies model development through a drag-and-drop interface. You will gain hands-on experience in constructing and optimizing classification models without extensive coding. By the end of this module, you will understand how to leverage Azure Machine Learning designer to build effective classification models, enabling you to tackle predictive tasks with confidence.

PRACTICE QUIZ: KNOWLEDGE CHECK

1. True or False?

Classification is a form of machine learning that is used to predict which category, or class, an item belongs to.

  • True (CORRECT)
  • False 

Correct: Classification is a form of machine learning that is used to predict which category, or class, an item belongs to.

2. You use Azure Machine Learning designer to create a training pipeline for a classification model. What must you do before deploying the model as a service?

  • Create an inference pipeline from the training pipeline (CORRECT)
  • Add an Evaluate Model module to the training pipeline 
  • Clone the training pipeline with a different name 
  • Correct: An inference pipeline must be created in order to deploy as a service.

3. A health clinic is planning on using datasets that contain characteristics of patients to predict whether the patient has a risk of diabetes or not. Can this task be accomplished with the help of classification?

  • Yes (CORRECT)
  • No 

Correct: In this case, the characteristics of the patient are the features, and the label is a classification of either 0 or 1, representing non-diabetic or diabetic.

4. What values/cases does a confusion matrix present?

Select all options that apply.

  • True positives (CORRECT)
  • False negatives (CORRECT)
  • True negatives (CORRECT)
  • False positives (CORRECT)

Correct: A confusion matrix shows the following cases: true positives, true negatives, false positives, false negatives.

Correct: A confusion matrix shows the following cases: true positives, true negatives, false positives, false negatives.

Correct: A confusion matrix shows the following cases: true positives, true negatives, false positives, false negatives.

Correct: A confusion matrix shows the following cases: true positives, true negatives, false positives, false negatives.

5. What are the two best metrics to assess model classification performance?

  • Accuracy
  • Precision (CORRECT)
  • Recall (CORRECT)

Correct: Most data scientists use metrics like precision and recall to assess classification model performance.

Correct: Most data scientists use metrics like precision and recall to assess classification model performance.

QUIZ: TEST PREP

1. Which metric presents the ratio of correct predictions (true positives + true negatives) to the total number of predictions?

  • Recall
  • Precision
  • Accuracy (CORRECT)
  • F1 Score

Correct: Accuracy presents the ratio of correct predictions (true positives + true negatives) to the total number of predictions.

2. You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model’s performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.6. What can you conclude about the model?

  • The model can explain 60% of the variance between true and predicted labels.
  • The model predicts accurately for 40% of cases
  • The model performs better than random guessing (CORRECT)
  • Correct: The higher the score of AUC, the better the performance of the model.

3. Which metric presents the fraction of positives cases correctly identified?

  • Accuracy
  • Precision (CORRECT)
  • F1 Score
  • Recall

Correct: Precision presents the fraction of positive cases correctly identified (the number of true positives divided by the number of true positives plus false positives)

4. Which of the following scenarios can be resolved by applying classification models?

  • A company who wants to predict the churn rate of their subscribers for next month.
  • A toy company wanting to determine which clients are inclined to buy a specific toy. (CORRECT)
  • A bank wanting to determine if a specific set of clients are eligible for taking a loan. (CORRECT)

Correct: Classification is a form of machine learning that is used to predict which category, or class, an item belongs to.

Correct: Classification is a form of machine learning that is used to predict which category, or class, an item belongs to.

5. Which of the following are models that help predict between two or several categories?

Select all that apply.

  • Linear Regression
  • Two-class decision forest (CORRECT)
  • Multi-class neural network (CORRECT)
  • Two-class logistic regression (CORRECT)

Correct: Two-class decision forests and Two-class logistic regressions help predict between two categories, while Multi-class neural networks help predict between several categories.

Correct: Two-class decision forests and Two-class logistic regressions help predict between two categories, while Multi-class neural networks help predict between several categories.

Correct: Two-class decision forests and Two-class logistic regressions help predict between two categories, while Multi-class neural networks help predict between several categories.

6. True or False?

Classification is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label.

  • True (CORRECT)
  • False

Correct: Classification is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label.

7. You are using Azure Machine Learning designer to create a training pipeline for a binary classification model.At some point, you want to separate the data into training and testing sets. Which model should you add to the pipeline?

  • Select columns in dataset
  • Split data (CORRECT)
  • Join data 

Correct: Split data module is particularly useful when you need to separate data into training and testing sets.

8. True or False?

Classification is an example of a supervised machine learning technique in which you train a model using data that includes features and unknown values.

  • False (CORRECT)
  • True

Correct: Classification uses features and known values to train a model.

CONCLUSION – Create A Classification Model With Azure Ai

In conclusion, classification is a crucial supervised machine learning technique for predicting categorical outcomes. Throughout this module, you have learned to create and optimize classification models using the Azure Machine Learning designer’s intuitive drag-and-drop interface. With this hands-on experience, you are now equipped to effectively apply classification techniques to various real-world scenarios. This understanding will enable you to confidently tackle predictive tasks, utilizing the powerful capabilities of Azure Machine Learning designer to build robust classification models.

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