INTRODUCTION – Create A Classification Model With Azure Ai
Classification is the supervised machine learning technique used to predict categorical output class labels purely based on trained input data. In this module, you are going to learn how to build classification models using the Azure Machine Learning designer, an extremely easy drag-and-drop interface that simplifies model development through user-friendly tools. So construct and optimize classification models with minimal coding, and you will experience the whole process in practice. By the end of this module, you will know how to use Azure Machine Learning designer and create powerful classification models to deal with predictive assignments aptly.
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 refers to a type of machine learning that is used for predicting the category or class of an item.
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 instance, the patient’s characteristics define the features and the label, representing classification that is 0 or 1, which is non-diabetic or diabetic, respectively.
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: Confusion matrix shows true positive, true negative, false positive, and false negative outcomes.
Correct: True positives, true negatives, false positives, and false negatives are the outcomes that are depicted in a confusion matrix.
Correct: The panic matrix features all sorts of scenarios such as true positives, true negatives, false positives, and false negatives.
Correct: The confusion matrix shows such models that have true positives, true negatives, false positives, and false negatives.
5. What are the two best metrics to assess model classification performance?
- Accuracy
- Precision (CORRECT)
- Recall (CORRECT)
Correct: Data scientists use metrics commonly known as precision and recall for evaluation of the performance of classification models.
Correct: Precision and recall are some of the measures used by data scientists in their evaluation of classification models.
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 is a proportion whereby right predictions, which include true positives and true negatives, make up the denominator with respect to the number of predictions done.
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: The ratio of true positive cases to the total number of true and false positive cases defines precision, or how many of those predicted positive were actually present.
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 kind of machine learning where a specific item will be assumed to fall into one of several predefined class categories.
Correct: Classification of items is the task in machine learning which tries to find out to which class an object belongs.
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 predict between the two categories or classes, while multi-class neural networks predict among more than two categories.
Correct: They predict results of outcome between 2 classes with the aid of the 2-class decision forests and 2-class logistic regressions as well as and the last one; multi-class neural networks used for predicting outcomes in more than 2 classes.
Correct: Two types of predictions with reference to binary events can be accomplished using a clinical decision forest or a binary logistic regression. It is known multi-class predictions are done using neural networks.
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: Classifications involve a model that has been built based on some data which contains the features and the corresponding label values. The model learns how to relate different combinations of features with the applicable 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: In particular, the Split Data module is useful for splitting data into training and test portions.
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 entails using characteristics and respective labels for training a model.
CONCLUSION – Create A Classification Model With Azure Ai
Ultimately, classification stands as a very critical supervised machine learning technique that is used for categorical aspirants. In this module, you have learnt to build and tune classification models with the utmost ease using the drag-and-drop feature of the Azure Machine Learning designer. Thus, equipped with this practical knowledge, you are now ready to apply classification techniques to problems in the outside world. You would be able to confidently deal with predictive issues, taking advantage of promising Azure Machine Learning designer functionalities to craft successful classification models.