Inference: Difference between revisions

Revision as of 17:43, 24 February 2023

See also: Machine learning terms

Introduction

In machine learning, inference refers to the process of using a trained model to make predictions or decisions about new data. The trained model takes in input data and produces output predictions based on its patterns learned from training data. Inference is essential for making a machine learning model into a practical application as it enables the model to be utilized for its intended purposes such as classifying images, creating text, or making recommendations.

Inference can be performed in real-time, where predictions are made as new data becomes available, or batch mode, where predictions are made for a large set of data all at once. Speed and accuracy in inference are crucial factors when applying machine learning models since they directly impact their usability and usefulness in practical applications.

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Machine learning models use inference, or making a guess based on what you have learned from examples. Imagine having pictures of animals and wanting to guess which kind is in a new picture that hasn't been seen before; using what you learned from looking at other images, you could use what was known before to make your guess. That's similar to what a machine learning model does - except instead of using your brain, it uses math instead!

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Let's pretend you own a toy box with many toys inside, such as cars, dolls and stuffed animals. When you want to play with one of them, you reach inside and grab one out - much like how a machine learning model "infers" or makes an assumption.

The machine learning model has encountered many examples of different things, just as you have in your toy box. When asked to make a prediction about something new, it reaches into its own "mind" to find the ideal toy to play with. Drawing upon all this knowledge from past examples, it makes an educated guess as to what the new thing might be.

Much like you might guess that the new toy is a stuffed animal based on what you've seen before in your toy box, the machine learning model makes predictions based on information it's seen. Just as sometimes you might be wrong and pick out a car instead of a stuffed animal, so too can this model make mistakes and provide incorrect answers. But the more examples it sees and plays with toys more frequently, the better equipped it becomes at making accurate predictions!

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 {{see also|Machine learning terms}}
 ==Introduction==
-In [[machine learning]], [[inference]] is when you make [[prediction]]s, decisions or [[generate content]] by applying a [[trained model]] to [[unlabeled example]]s or [[prompts]].
+In machine learning, [[inference]] refers to the process of using a [[trained model]] to make [[prediction]]s or decisions about new [[data]]. The trained model takes in [[input data]] and produces [[output]] predictions based on its patterns learned from [[training data]]. Inference is essential for making a [[machine learning model]] into a practical [[application]] as it enables the model to be utilized for its intended purposes such as [[classifying images]], [[creating text]], or [[making recommendations]].
-==Types of Inference in Machine Learning==
+Inference can be performed in real-time, where predictions are made as new data becomes available, or batch mode, where predictions are made for a large set of data all at once. Speed and accuracy in inference are crucial factors when applying machine learning models since they directly impact their usability and usefulness in practical applications.
-In machine learning, there are two primary forms of inference: supervised and unsupervised.
-Supervised inference is employed when the training data used to train a model includes labeled examples, meaning that each desired output can be predicted based on its input features. Common applications of supervised inference include image classification, speech recognition and natural language processing.
-Unsupervised inference, on the other hand, occurs when data used to train a model does not include labeled examples. In this scenario, the model learns to detect patterns or relationships in input data without being explicitly told what its desired output should be. Common applications of unsupervised inference include clustering, dimensionality reduction and anomaly detection.
-==The Inference Process in Machine Learning==
-Machine learning typically involves several steps for inference:
-. Data Preparation: In this step, input data is cleaned, transformed and organized so it can be fed into the model.
-. Model Selection: This step involves selecting an appropriate model based on the inference task, input data and desired outputs.
-. Model Training: In this step, the model is trained using prepared input data through optimization to adjust its parameters so that its predictions are as precise as possible.
-. Model Evaluation: Finally, the trained model is evaluated against a set of test data to assess its accuracy and identify any potential issues.
-. Model Deployment: Finally, the trained model is deployed into a production environment so it can be utilized to make predictions on new data that has yet to be collected.
 ==Explain Like I'm 5 (ELI5)==