A Clustering Model With Azure Machine Learning Designer Flashcards
Introduction:
You can use Microsoft azure machine learning designer to create clustering models by using a drag-and-drop visual interface without needing to write any code
Identifying clustering machine learning scenarios caroline
Clustering is a form of machine learning that is used to group similar items into clusters based on their features.
For example a researcher might take measurements of penguins and group Them based on similarities in their proportions.
Clustering is an example of unsupervised machine learning in which you train a model to separate items into clusters based purely on their characteristics or features.
There is no previously known faster value or label from which to train the model.
Scenarios for clustering machine learning models:
Clustering machine learning models are used in many Industries a few scenarios are:
Chrysler customer attribute data into segments for marketing analysis.
Plus the Geographic coordinates into regions of high traffic in a city for a ride share application.
Cluster written feedback into topics or prioritise customer service changes.
Understand steps for clustering:
You can think of the steps to train and evaluate a clustering machine learning model as:
One prepare data Caroline identify the features in a dataset full-stop reprocess or clean and transform the data as needed.
To train model kolin split the dart into two groups a train and validation set. Train a machine learning model using the training darts it. Testing machine learning model for performances in the validation dataset. Evaluate performance
Deploy a protective service.
Prepared after:
To train a clustering model you need a data set that includes multiple observations of the items you want to cluster including numeric features that can be used to determine similarities between individual cases that will help separate them into clusters.
As a machine learning designer has several pre-built components that can be used to prepare data for training.
These components enable you to clean data normalise features join tables and more.
Clustering train model:
To train a clustering model you need to apply a clustering algorithm to the data using only the features that you have selected for clustering.
You’ll train the model with a subset of the data and use the rest to test the trained model.
The k-means clustering algorithm groups items into the numbers of clusters or centroids you specify- value referred to as a full stop
The k-means algorithm works by:
Initialising cake ordinance as randomly selected points called centroids in in-dimensional space where n is the number of dimensions in the feature vectors.
Plotting the feature vectors as points in the same space and assigning each point to its closest centroid.
Three moving the centroid to the middle of the points allocated to it based on the mean distance. Reassigning the points to the closest centroid after the move.
Repeating steps three and four until the cluster a location stabilize or the specified number of iterations has completed.
You will use designers assign data to cross this component to group the data into clusters.
Once you connect all the components you will want to run an experiment which will use the data asset on the canvas to train a model
Evaluate performance:
After training and model it’s important to evaluate its performance. There are many performance metrics and methodologies for evaluating how well a model makes predictions.
You can review evaluation metrics on the complete the job page by right clicking on the evaluate model component
When the experiment has finished select job details. Right click on the evaluate model module and select preview data then select evaluation results. These metrics can help the other scientists assess how well the model separates the clusters.
They include a row of metrics for each cluster a summary row for combined evaluation. The metrics in each row as follows
Metrics for each row as follows:
Average distance to other Center Cole on this indicates how close on average each point in the cluster is to the centroids of all the other clusters.
Average distance to cluster centre kalan this indicates how close on average each point in the cluster is to the centroid of the cluster.
Number of points scored on the number of points assigned to the cluster.
Maximum distance to Gloucester Center close on the maximum of the distances between each point and the centroid of that points cluster. If this number is high the cluster may be widely dispersed. This statistic is in combination with the average distance to cluster centre helps you determine the clusters spread.
Deploy a predictive service:
You have the ability to deploy a service that can be used in real-time.
In order to automate your model into a service that makes continuous predictions you need to create and deploy an inference pipeline.
Inference pipeline:
To deploy your pipeline you must first convert the training pipeline into a real-time influence pipeline. This process removes training components and adds web service inputs and outputs to handle request.
The entrance pipeline performs the same data transformation was the first pipeline for new data.
Then it uses the trained model or to infer or predict plastids based on its features. This model will form the basis for a protective service that you can publish for your applications to use.
Deployment turn on new line after creating the inference pipeline you can deploy it as an Endpoint. In the endpoints perjury can view deployment details test your Pipeline Services sample data and find rentals to connect your pipeline service to a client application.
On the test tab you can test you deployed service with sample data in a dress on format. You can find credentials for your service on the consumer to have. These credentials are used to connect to a trained machine learning model as a service to a client application.
Knowledge check:
Question: you are using an azure machine learning designer pipeline to train and test-a-kay-means clustering model. You want your model to assign items to one of three clusters. Which configuration property of the cave-means clustering module should use it to accomplish this?
Set number of centroids: three
Knowledge track:
Question: you use azure machine learning designer to create a training pipeline for a clustering model. Now you want to use the model in an inference pipeline. Which model should you use to info cluster predictions from the model?
Assign data to klosters-hughes the assigned to the clusters module to generate cluster predictions from a trained clustering model
