ModelBuilder is an application you use to create, edit, and manage models. Models are workflows that string together sequences of geoprocessing tools, feeding the output of one tool into another tool as input. ModelBuilder can also be thought of as a visual programming language for building workflows.
Build a model by adding and connecting data and tools; Iteratively process every feature class, raster, file, or table in a workspace. Visualize your workflow sequence as an easy-to-understand diagram. Run a model step-by-step, up to a selected step, or run the entire model. ModelBuilder is an application you use to create, edit, and manage models. Models are workflows that string together sequences of geoprocessing tools, feeding the output of one. SketchUp is a premier 3D design software that truly makes 3D modeling for everyone, with a simple to learn yet robust toolset that empowers you to create whatever you can imagine. For scale model buildings, dioramas and train layouts, software is the perfect way to design scenery to your exact, desired specifications, including: Windows; Billboards; Road signs; Model building textures; We also make it easy for you to import your own pictures or images that you find from Internet searches and create unique architectural models with our comprehensive model builder. Evan Designs Model Builder Make the buildings you cannot buy and print them out on your printer. Use Model Builder building software to inexpensively add hundreds of quality paper buildings to your layout.
While ModelBuilder is very useful for constructing and executing simple workflows, it also provides advanced methods for extending ArcGIS functionality by allowing you to create and share your models as tool.
ModelBuilder can even be used to integrate ArcGIS with other applications. An example is provided below:
The above model is used by a municipality to send e-mail notifications to all addresses within 1 mile of an address for which a building permit application is filed. The model starts with a feature class of multiple permit application point locations. This feature class is fed into an iterator that loops over each individual point and feeds the point into the Select Layer By Location tool, where all addresses (parcels) within 1 mile of the point are selected. These addresses are then passed to a custom script tool (one that you or your colleague created), Generate Mailing List, that executes Python code to output a mailing list in HTML format. Finally, the mailing list is fed to another custom script tool, Send Email Notifications, which runs a custom executable that sends e-mail notifications and produces a success code.
The benefits of ModelBuilder can be summarized as follows:
If you have never used ModelBuilder, start with the Executing tools in ModelBuilder tutorial.
If interested in creating custom tools with ModelBuilder, see A quick tour of creating tools with ModelBuilder and the Creating tools with ModelBuilder tutorial.
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ML.NET Model Builder is an intuitive graphical Visual Studio extension to build, train, and deploy custom machine learning models.
Model Builder uses automated machine learning (AutoML) to explore different machine learning algorithms and settings to help you find the one that best suits your scenario.
You don't need machine learning expertise to use Model Builder. All you need is some data, and a problem to solve. Model Builder generates the code to add the model to your .NET application.
Scenario
You can bring many different scenarios to Model Builder, to generate a machine learning model for your application.
A scenario is a description of the type of prediction you want to make using your data. For example:
Which machine learning scenario is right for me?
In Model Builder, you need to select a scenario. The type of scenario depends on what type of prediction you are trying to make.
Text classification
Classification is used to categorize data into categories.
Value prediction
Regression is used to predict numbers.
Image classification
Image classification is used to identify images of different categories. For example, different types of terrain or animals or manufacturing defects.
You can use the image classification scenario if you have a set of images, and you want to classify the images into different categories.
Object detection
Object detection is used to locate and categorize entities within images. For example, locating and identifying cars and people in an image.
You can use object detection when images contain multiple objects of different types.
Recommendation
The recommendation scenario predicts a list of suggested items for a particular user, based on how similar their likes and dislikes are to other users'.
You can use the recommendation scenario when you have a set of users and a set of 'products', such as items to purchase, movies, books, or TV shows, along with a set of users' 'ratings' of those products.
Environment
You can train your machine learning model locally on your machine or in the cloud on Azure, depending on the scenario. Moccanote v1 0 0.
When you train locally, you work within the constraints of your computer resources (CPU, memory, and disk). When you train in the cloud, you can scale up your resources to meet the demands of your scenario, especially for large datasets.
Local CPU training is supported for all scenarios except Object Detection.
Local GPU training is supported for Image Classification.
Azure training is supported for Image Classification and Object Detection.
Data![]()
Once you have chosen your scenario, Model Builder asks you to provide a dataset. The data is used to train, evaluate, and choose the best model for your scenario.
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Model Builder supports datasets in .tsv, .csv, .txt formats, as well as SQL database format. If you have a .txt file, columns should be separated with
, , ; or /t and the file must have a header row.
If the dataset is made up of images, the supported file types are
.jpg and .png .
For more information, see Load training data into Model Builder.
Choose the output to predict (label)
A dataset is a table of rows of training examples, and columns of attributes. Each row has:
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For the house-price prediction scenario, the features could be:
The label is the historical house price for that row of square footage, bedroom, and bathroom values and zip code.
Example datasets
If you don't have your own data yet, try out one of these datasets:
Train
Once you select your scenario, environment, data, and label, Model Builder trains the model.
What is training?
Training is an automatic process by which Model Builder teaches your model how to answer questions for your scenario. Once trained, your model can make predictions with input data that it has not seen before. For example, if you are predicting house prices and a new house comes on the market, you can predict its sale price.
Because Model Builder uses automated machine learning (AutoML), it does not require any input or tuning from you during training.
How long should I train for?
Model Builder uses AutoML to explore multiple models to find you the best performing model.
Longer training periods allow AutoML to explore more models with a wider range of settings.
The table below summarizes the average time taken to get good performance for a suite of example datasets, on a local machine.
These numbers are a guide only. The exact length of training is dependent on:
It's generally advised that you use more than 100 rows as datasets with less than that may not produce any results and may take a significantly longer time to train.
Evaluate
Evaluation is the process of measuring how good your model is. Model Builder uses the trained model to make predictions with new test data, and then measures how good the predictions are.
Model Builder splits the training data into a training set and a test set. The training data (80%) is used to train your model and the test data (20%) is held back to evaluate your model.
How do I understand my model performance?
A scenario maps to a machine learning task. Each ML task has its own set of evaluation metrics.
Value prediction
The default metric for value prediction problems is RSquared, the value of RSquared ranges between 0 and 1. 1 is the best possible value or in other words the closer the value of RSquared to 1 the better your model is performing.
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Other metrics reported such as absolute-loss, squared-loss, and RMS loss are additional metrics, which can be used to understand how your model is performing and comparing it against other value prediction models.
Classification (2 categories)
The default metric for classification problems is accuracy. Accuracy defines the proportion of correct predictions your model is making over the test dataset. The closer to 100% or 1.0 the better it is.
Other metrics reported such as AUC (Area under the curve), which measures the true positive rate vs. the false positive rate should be greater than 0.50 for models to be acceptable.
Additional metrics like F1 score can be used to control the balance between Precision and Recall.
Classification (3+ categories)
The default metric for Multi-class classification is Micro Accuracy. The closer the Micro Accuracy to 100% or 1.0 the better it is.
Another important metric for Multi-class classification is Macro-accuracy, similar to Micro-accuracy the closer to 1.0 the better it is. A good way to think about these two types of accuracy is:
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More information on evaluation metrics
For more information, see model evaluation metrics. Wondershare allmymusic 3 0 1 5.
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If your model performance score is not as good as you want it to be, you can:
Code
After the evaluation phase, Model Builder outputs a model file, and code that you can use to add the model to your application. ML.NET models are saved as a zip file. The code to load and use your model is added as a new project in your solution. Model Builder also adds a sample console app that you can run to see your model in action.
In addition, Model Builder outputs the code that generated the model, so that you can understand the steps used to generate the model. You can also use the model training code to retrain your model with new data.
What's next?
Install the Model Builder Visual Studio extension
Try price prediction or any regression scenario
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