My task in a machine learning project is application modeling What does this term "modeling" mean? 1- Draw Diagrams and ..? 2- Explain the functionalities of the application in sentences? 3-Something else please answer me
A “model” in machine learning is the output of a machine learning algorithm run on data. A model represents what was learned by a machine learning algorithm.
Furthermore, the following links may be helpful:
https://machinelearningmastery.com/difference-between-algorithm-and-model-in-machine-learning/#:~:text=A%20model%20represents%20what%20was,structures%20required%20to%20make%20predictions.
https://towardsdatascience.com/how-to-build-a-machine-learning-model-439ab8fb3fb1
https://towardsdatascience.com/all-machine-learning-models-explained-in-6-minutes-9fe30ff6776a
In general, a Machine Learning Project has various Stages.
In the first phase, strategy is outlined.
Then is Gathering Data, Dataset preparation and preprocessing.
Dataset splitting in Train, Test and Validation.
Then comes the Modeling.
>>Evaluate and Trains with numerous models to see which model performs best.
>>Model training
>>Model evaluation and testing
ML models can be categorized as either supervised or unsupervised. If the model is a supervised model, it’s then sub-categorized as either a regression or classification model.
What is the task you are trying to do? Prediction of output according to many input variables, predicting a particular class, clustering?
Ultimately, it is expected to have a model that can be used to predict output when having unseen data as input..
A “model” in machine learning is the output of a machine learning algorithm run on data. A model represents what was learned by a machine learning algorithm.
Furthermore, the following links may be helpful:
https://machinelearningmastery.com/difference-between-algorithm-and-model-in-machine-learning/#:~:text=A%20model%20represents%20what%20was,structures%20required%20to%20make%20predictions.
https://towardsdatascience.com/how-to-build-a-machine-learning-model-439ab8fb3fb1
https://towardsdatascience.com/all-machine-learning-models-explained-in-6-minutes-9fe30ff6776a
Related to your query, I suggest you to follow the attached fundamental book on Machine Learning.
i think these resources can help you and understand terms
https://docs.bmc.com/docs/discovery/112/start-anywhere-application-modeling-788122565.html?scroll-versions:version-name=11.3
https://www.infoq.com/articles/application-models-software/
Means how to represent real-world variables using data. For example a color of an image can be modeled using a histogram. So the histogram, represented as a mathematical vector, is a model for the actual variable which is color.
I like the links by Zahra Taheri
A model can be thought of as a Function. The model takes in Input data, and produces Output predictions.
"Modelling" means "creating a good model".
If you are using machine learning, then you start by creating a random model, and you train it to produce good Predicted Outputs by repeatedly showing it Inputs together with Correct Outputs. You change the model gradually so that the Predicted Outputs match the Correct Outputs. Then you can use the trained model to make new predictions on Inputs where you don't already know the Outputs.
A model in the Machine Learning environment is creating a pattern of traits or attributes that a model you are developing will learn from , being recognized as efficient in terms of improving future improvement, based on previous learning errors .
Just like human beings learn from past errors(hopefully), Machines learn, by their programmers to correct past errors that should not be part of aspired behavior. You can develop a model which will contribute to artificial intelligence, in learning and rewarding behavior positive behavior, and keep refining this model till no deviations occur. It is really like established a desired baseline of positive traits, where the goal is , few negative errors as possible.
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