Inference "Inference" is applying a trained model to unlabeled samples to obtain the corresponding targets. In other words, "inference" is the process of making predictions using a model.
@abacusai
-
Regression Models for Predicting Continuous Values
By
–
Regression A regression model predicts continuous values, for example: • the value of a house
• the price of a stock
• tomorrow's temperature -
Validation: Assessing Model Performance on Unseen Data
By
–
Validation Validation is the process that lets us know whether a model is any good. Usually, we run a set of (unseen) labeled samples through a model to ensure that it can predict the targets.
-
Understanding Models: Features, Rules, and Target Predictions
By
–
Model A model defines the relationship between features and the target. You can think of a model as a set of rules that, given certain features, determine the corresponding target. For example, given the bedrooms, bathrooms, and square footage, we get the price.
-
Training: Building Models from Labeled Data
By
–
Training Training is a process that builds a model. We take labeled samples during training and let the model gradually learn the relationships between features and the target.
-
Unlabeled Samples in Supervised Learning: Features Without Targets
By
–
Unlabeled sample Unlabeled samples contain features, but they don't contain the target: (x, ?) The goal of supervised learning is to build a model that predicts the target of unlabeled samples.
-
Samples in Machine Learning: Labeled and Unlabeled Data
By
–
Samples (also known as "examples" or "instances") A sample is a particular instance of data. It could be "labeled" (when it specifies the target) or "unlabeled" (when it doesn't.)
-
Labeled Samples: Training Data with Features and Targets
By
–
Labeled sample Labeled samples are used to train and validate a model. These are usually represented as (x, y), where "x" is a vector containing all the features, and "y" is the corresponding target.
-
Understanding Features in Machine Learning Prediction Models
By
–
Features (also referred to as "x" or "variables") These are the input variables to our problem. We use these features to predict the target. For example: • pixels of a picture
• number of bedrooms of a house
• square footage of a house