Input and Output
A machine learning model receives information as input and produces a prediction as output. The features usually form the input, while the output is the result the model is trying to predict.
Input goes into the model. Output comes from the model.
In supervised learning, the input is made from features. The model learns from examples where the correct output is already known, and then uses what it learned to predict the output for new data.
What Is Input?
Input is the information we give to the machine learning model.
This information is usually represented by features.
2,000 sq ft
3
City A
These features give the model information about the house.
What Is Output?
Output is the result produced by the model.
When we are training a supervised learning model, we already know the correct output for the training examples. That known answer is called the label or target.
So, for house price prediction, the output is the price predicted by the model.
Input → Model → Output
The entire idea can be reduced to one simple flow.
Size, bedrooms, location
Uses what it learned
House price
House Price Example
Imagine we have a house with the following information:
These are the inputs to the model.
The model uses the input features to produce the prediction.
Training Input vs Prediction Input
There is an important difference between training and making a prediction for a new example.
The model sees features together with the correct label and learns the relationship.
We give the model new features and ask it to predict the output.
This distinction is important. If the answer is already known for a training example, we can use it to teach the model. For a new example, the answer is what we want the model to predict.
Customer Purchase Example
Let's use another example to make the idea clearer.
An online store wants to predict whether a customer will purchase a product.
The customer information is the input. The model's prediction — "Yes" — is the output.
Output Depends on the Problem
The output is not always the same type of value. It depends on what we are trying to predict.
Numerical output.
Category output.
Category output.
The input-output structure stays the same, but the type of output changes depending on the machine learning problem.
Features, Labels, Input, and Output
Now we can connect the terms we have learned.
Input → Model → Output
Features provide the input information. The model uses learned patterns to produce an output. In supervised learning, the known output in the training data is the label or target.
Identify the Input and Output
We want to predict the price of a house.
The house size and bedrooms are input features. The predicted house price is the model's output.