Inputs, Features, and Targets
Explore the key concepts of inputs features and targets in supervised learning. Understand how models use processed features from raw data to learn and predict outcomes. Learn about feature selection and its impact on model performance in tasks like facial recognition and activity classification.
Before a machine learning model can learn anything, it needs structured data to observe patterns. In supervised learning, we train a model using examples where we provide both the raw information and the desired outcome.
The input is the raw data presented to the model.
The features are the specific, measurable pieces of information extracted from the inputs that the model learns from.
The target is the desired output that the model is trying to predict or classify.
Feature magic in machine learning
Under the umbrella of machine learning, inputs, and features are highly correlated because the input (commonly called the dataset) is processed to derive more insightful features. For example, the input will be the image of the person we want to identify for a facial recognition application. Features like edges, texture patterns, distance between the eyes, the nose, the height, etc., can be extracted by applying different transformations over the input image. Finally, the target will be the name of the person. In order to create this application, we need to make our model learn the identities of people, and for that, we need to provide the model with a mapping function between the inputs and the identities.
Extracting the input’s insightful features helps the model learn faster and more accurately. Our model might use the picture’s brightness or standard deviation as a feature, or it might divide the image into small patches like the four-quadrant system. In this manner, we get local information that comes in handy when the model struggles to understand the whole picture. Facial recognition is typically a classification task, where the model classifies the input image into one of ...