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(Created page with "{{see also|Machine learning terms}} ===Definition of Inference in Machine Learning== Machine learning refers to the process of inference, or making predictions or decisions based on data and a trained model. The goal is for the model to generate outputs as accurate as possible given its input data, then use these outputs to make decisions or predictions about new, unseen data. ==Types of Inference in Machine Learning== In machine learning, there are two primary forms of...")
 
 
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{{see also|Machine learning terms}}
{{see also|Machine learning terms}}
===Definition of Inference in Machine Learning==
==Introduction==
Machine learning refers to the process of inference, or making predictions or decisions based on data and a trained model. The goal is for the model to generate outputs as accurate as possible given its input data, then use these outputs to make decisions or predictions about new, unseen data.
In [[machine learning]], [[inference]] is when you make [[prediction]]s or [[generate content]] by applying a [[trained model]] to [[new data]] such as [[unlabeled examples]] or [[prompts]].


==Types of Inference in Machine Learning==
==Inference Process==
In machine learning, there are two primary forms of inference: supervised and unsupervised.
Inference in machine learning involves several steps. First, the trained model is loaded into memory and then new data is fed into it. Afterward, the model utilizes [[parameters]] and [[functions]] learned from its [[training data]] to make predictions or decisions about this new data.


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.
==Types of Inference==
In machine learning, there are two main types: [[real-time inference]] and [[batch inference]].  


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.
#[[Real-time inference]] refers to making predictions as new data is collected; this approach works best when the model must respond quickly to changes such as [[image recognition|image]] or [[speech recognition]] systems.  
#[[Batch inference]] on the other hand involves making predictions for a large [[dataset]] at once and is commonly employed when models don't need to respond in real-time like [[recommendation system]]s do.


==The Inference Process in Machine Learning==
==Considerations for Inference==
Machine learning typically involves several steps for inference:
Speed and accuracy of inference are critical factors when using machine learning models. Speed of inference is especially crucial in real-time applications since it determines the model's capability to respond rapidly to changing data. On the other hand, [[accuracy]] inference has an impact on all applications since it determines the usefulness and dependability of predictions made by the model.
 
1. Data Preparation: In this step, input data is cleaned, transformed and organized so it can be fed into the model.
 
2. Model Selection: This step involves selecting an appropriate model based on the inference task, input data and desired outputs.
 
3. 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.
 
4. Model Evaluation: Finally, the trained model is evaluated against a set of test data to assess its accuracy and identify any potential issues.
 
5. 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)==
==Explain Like I'm 5 (ELI5)==
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!
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!


==Explain Like I'm 5 (ELI5)==
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]
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!
 
 
[[Category:Terms]] [[Category:Machine learning terms]]
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