INTRODUCTION – Create A Clustering Model With Azure Ai
Explore when clustering is done in an unsupervised manner using different methods for machine learning to classify groups of other entities. These groups always have associated typicalities between them while allowing for pattern or structural identification and derivation from a data set without any pre-classified labels. During this module, you will learn how to perform clustering model building using Azure Machine Learning Designer, which is defined as drag-and-drop in designing and deploying.
Get away from any of the other types of clustering algorithms by gaining practical learning experience in debugging and selecting models that suit a data type. At the end of this module, you will be able to apply the Azure Machine Learning designer to build great clustering models to discover and mine your data.
PRACTICE QUIZ: KNOWLEDGE CHECK
1. True or False?
Clustering is a form of machine learning that is used to group similar items into clusters based on their predictions.
- True
- False (CORRECT)
Correct: Clustering is an advanced technique in machine learning that allows fine differentiation among similar items put together into groups based on their own characteristics.
2. What type of machine learning technique does clustering examples?
- Reinforcement
- Supervised
- Unsupervised (CORRECT)
Correct: Clustering comes under the category of unsupervised learning machine.
3. You create a machine learning experiment based on a clustering model. Now you want to use the model in an inference pipeline. Which module should you use to infer cluster predictions from the model?
- Train clustering model
- Assign data to clusters (CORRECT)
- Score model
Correct: The “Assign Data to Clusters” module allocates cluster assignments to your data according to your existing trained clustering model.
4. When using a clustering module, what algorithm let’s you group items into a number of clusters you specify?
- C-Means algorithm
- B: L-Means algorithm
- K-Means algorithm (CORRECT)
Correct: The items are divided into a certain number of clusters by the K-Means function.
5. Suppose you are testing a K-Means clustering model. If you would want your model to assign items to one of four clusters, which parameter/property should you configure on the module?
- Set random seed to 4
- Set stratified split
- Set number of centroids to 4 (CORRECT)
Correct: So, if the number of centroids is increased, then you can increase the number of clusters.
QUIZ: TEST PREP
1. Which of the following is a clustering algorithm?
- K-Means (CORRECT)
- Two-Class Neural Network
- Two-Class Logistic Regression
Correct: K-Means is an algorithm that associates similar objects to groups.
2. What is the purpose of a clustering model?
- Makes forecasts by estimating the relationship between values
- Answers simple two-choice questions
- Separates similar data points into intuitive groups (CORRECT)
Correct: The goal of clustering models is to group similar data elements into clusters that are meaningful.
3. Which of the following scenarios can be resolved by applying clustering modules/algorithms?
Select all that apply.
- A bike rental company that wants to predict the number of customers for the next day so that it will assure the necessary staff and cycles.
- A radio company that wants to apply tags (like rock, pop, R&B etc) to songs or artists. (CORRECT)
- A social media company that wants to group similar users based on their posts. (CORRECT)
Correct: Clustering models are devised to segregate similar units into different, significant groups.
Correct: The main aim of clustering models is to make meaningfully intuitive categories of similar data points.
4. When evaluating a clustering model, what metrics can you visualize in the Evaluate results section?
Select all that apply.
- Average distance to cluster center (CORRECT)
- Maximal distance to cluster center (CORRECT)
- Average distance to other center (CORRECT)
- Number of points (CORRECT)
Correct: Evaluation Results contain Metrics: average distance of other centers, average distance of CLUSTER CENTER, number of points, and maximal distance from CLUSTER CENTER here in the Clustering module.
Correct: For every clustering module, the Evaluate Results section presents some conclusion metrics such as the average distance to further centers, average distance to the cluster center, the number of points in the cluster, and maximum distance to the cluster center.
Correct: The Evaluate Result’s section of cluster modules shows metrics like average distance to other center and average distance to center of the cluster, number of points, and maximum distance to the cluster center.
Correct: The evaluation of the results from an association clustering module contains metrics like average distances to other centers, average distances from a point to the cluster center, count of points, and maximum distances to the cluster center.
5. You are building an Azure Machine learning pipeline that involves a clustering module. You need to prepare the data and change some of the numeric values from the dataset to use a common scale, without distorting differences in the ranges of values or losing information.
Which module should you apply?
- Normalize Data (CORRECT)
- Split data
- Edit metadata
Correct: Normalization refers to the transformation of numeric columns in datasets to a common scale so that differences in value ranges are current while still preserving information.
6. True or False?
Clustering is an example of supervised machine learning, in which you train a model to separate items into clusters based purely on their characteristics or features.
- True
- False (CORRECT)
Correct: Clustering would come under the category of unsupervised machine learning. The training of models involves grouping the objects into clusters based on their characteristics or features without any supervision.
7. A Hospital Care chain wants to open a series of Emergency-Care wards within a region. The chain knows the location of all the maximum accident-prone areas in the region. They have to decide the number of the Emergency Units to be opened and the location of these Emergency Units, so that all the accident-prone areas are covered in the vicinity of these Emergency Units.
Which type of machine learning model is best to be applied in this scenario?
- Regression
- Classification
- Clustering (CORRECT)
Correct: Clustering models work to represent points in space by informative groupings, or clusters. For example, clustering can be used to categorize accidents according to the emergency units responding to the accident.
8. You want to train a model where there is no previously known cluster value (or label) from which to train the model.
Which type of machine learning would you use?
- Unsupervised machine learning (CORRECT)
- Supervised machine learning
Correct: It belongs to type of unsupervised learning in which model learns to cluster the items in groups based on their features or characteristics without the requirements of pre-existing labels or clusters for the input samples.
CONCLUSION – Create A Clustering Model With Azure Ai
In summary, clustering is an important unsupervised machine learning method, grouping similar entities into clusters based on their features, thereby assisting to reveal patterns and structures in unlabeled data. In this module, you have been involved on hands-on practice on creating and optimizing a clustering model using the user-friendly Azure Machine Learning designer. Experimenting with different algorithms and refining models makes one ready to utilize this great tool effectively in constructing robust clustering models and confidently extracting valuable insights and patterns from one’s data.