Create A Regression Model With AZ Machine Learning Designer

Question 1

Regression colon is a supervised machine learning technique used to predict the numeric values.

Question 2

You can use Microsoft azure machine learning designer to create regression models by using a drag-and-drop visual interface without needing to write any code.

Answer 2

In this module you will learn how to:

Identify regression machine learning scenarios
Using machine learning designer to train a regression model
Use a regression model for influencing
Deploy a regression model as a service

Question 3

Identify regression machine learning scenarios: Regression is a form of machine learning used to understand the relationships between variables and to protect their desired outcome.
Regression predicts a numeric label or outcome based on variables or features. For example and auto mobile sales company might use the characteristics of a car bracket such as engine size number of seats mileage and sell one plus (to predict it’s likely selling price.
In this case the characteristics of the car are the features and the selling price is the label.

Answer 3

Regression:
is an example of a supervised machine learning technique in which you train a model using data that includes both the features and and values for the label so that the model lens to fit the feature combinations to the label.
Then after training has been completed you can use the trained model to predict labels for a new items for which the label is unknown

Question 4

Scenarios for regression machine learning models: Using characteristics of houses such as square footage and number of rooms to predict home prices
Using characteristics of Farm conditions such as weather and soil quality to predict crop yield
Using characteristics of a past campaign such as advertising logs to predict future advertisement clicks

Question 5

AZ machine Learning Studio line
Az machine Learning Studio is a web portal for machine learning Solutions in az.
It includes a wide range of features and capabilities that helps data scientist prepare data train models public publish predictive services and monitor their usage. To begin using the web portal you need to assign the Workspace you created in the AZ portal to AZ machine Learning Studio

Question 6

AZ machine learning computer code on new line at its core AZ machine learning is a service for training and managing machine learning models for what you need computer resources on which to run the training process.
Compute targets are cloud-based resources on which you can run model training and data exploration processes.

Question 7

Nav machine Learning Studio you can manage the computer August 4 your data Science activities.
There are four kinds of a computer resource you can create

Answer 7

Compute instances
Computer clusters
Insurance clusters
Attached compute

Question 8

AZ machine learning designer: Nav machine Learning Studio there are several ways to also regression machine learning models.
One way is to use a visual interface called designer that you can use to train test and deploy machine learning models.
The drag-and-drop interface makes use of clearly defined inputs and outputs that can be shared reused and version controlled.

Answer 8

Designer projects also known as pipelines:
Each design a project known as a pipeline has a lift panel for navigation and a canvas on your right hand side.
To use designer identify the building blocks or components needed for your model place and connect them on your canvas and run a machine learning job.

Question 9

Pipelines:
Pipelines that you organise manage and reuse complex machine learning workflows across projects and uses.
A pipeline starts with the dataset from which you want to train the model. Each time you run a pipeline the configuration of the pipeline and its results are stored in your Workspace as a pipeline job

Question 10

Components:
And easy machine learning component encapsulates one step in a machine learning pipeline. You can think of a component as a programming function and as a building block for a z machine learning pipelines.
In a pipeline project you can access data assets and components from the left panels asset library tab

Question 11

Datasets:
You can create data assets on the data page from local files a data store web files and open datasets.
These data assets will appear along this with the standard sample data sets in designers asset library

Question 12

Azure machine learning job:
an azure machine learning job executes a task against a specified computer target.
Jobs enable systematic tracking for all your machine learning experimentation and workflows. Once a job is created AZ machine learning maintains a run record for the job.
All of your jobs run records can be viewed in AZ machine learning studio.
In your design a project you can access the status of a pipeline job using the submitted jobs to have on the left pane

Question 13

Steps for regression turn on new line you can think of the steps to train and evaluate a regression machine learning model as: 1 prepared article on identify the features and label in a dataset full-stop free process or clean and transform the data as needed.
To train model: split the data into two groups a training and evaluation said trainer machine learning model using the training dataset fullstop test the machine learning model for performance using the validation data set

Answer 13

Trademodel
Split the data into two groups a training and validation set
Train a machine learning model using the training data set
Test the machine learning model for performance using the validation dataset.

Question 14

Prepare data:
Is 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

Question 15

Train model:
To train a regression model you need a data set that includes historical features characteristics of The Entity for which you want to make a prediction and known label values.
The label is the quantity you want to train a model to predict. It is common practice to train the model using a subset of the data while holding back some data with which to test the trained model. This enables you to compare the labels that the model predicts with the actual known labels in the original dataset

Answer 15

You will use designer school model component to generate the predicted class labelvalue. Once you connect all the components you will want to run an experiment which will use the data as set on the canvas to train and score a model

Question 16

Evaluate performance caroline
After training and model it is 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 completed job page by right clicking on the evaluate model component

Question 17

Mean absolute error (maa):
The average difference between predicted values and true values.
The value is based on the same units as the label in this case dollars. The lower this value is the better the model is predicting.
Root mean Square error (are msy): The square root of the main Square difference between predicted and true values.
The result is a metric based on the same unit as the label. When compared to the m a y a larger different indicates greater variance in theatre in the individual errors for example with some areas being very small or others are large

Answer 17

Relative squared error (are SE) Caroline new line a relative metric between one and zero based on the square of the differences between predicted and true values.
The closer 20 this metric is the better the model is performing. Because this metric is relative it can be used to compare models where the labels are in different units. Relative absolute error(Rae) :
Relative metric between 0 and 1 based on the absolute differences between predicted and true values. The closer 20 this metric is the better the model is performing. Like rsys metric can be used to compare models where the labels are in different units

Question 18

Coefficient of determination (r squared plus): This metric is more commonly referred to as are dash squared and summarises how much of the variance between predicted and true values is explained by the model.
The closer to 1 this value is the better the model is performing.

Question 19

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 interface pipeline.

Question 20

Interface pipeline:
To deploy your pipeline you must first convert the training pipeline into a real-time inference pipeline.
This process removes training components and adds web service inputs and outputs to handle requests.
The entrance pipeline performs the same data transformation does the first pipeline for new data. Then it uses the trains model to infer or predict label values based on its features. This model will form the basis for a predictive service that you can publish for applications to use

Question 21

Deployment:
After creating the inference pipeline you can deploy it as an Endpoint. In the endpoints page you can view development but details test your pipeline service with sample data and find credentials to connect your pipeline service to a client application.
It will take a while for your input to be deployed the deployment state on the details table indicate healthy when deployment is successful

Answer 21

On the test tab you can test your deployed service with the sample data in a jsonformat. The Test Tube is a tool you can use to quickly check to see if your model is behaving as expected full-stop typically it is helpful to test the service before connecting it to an application.
You can find credentials for your service on the consume tab. These credentials are used to connect your trained machine learning model as a service to a client application