Categories → Causal Wizard Concept , Process , Causal Inference , Statistics , Study Design , Method
A machine learning model is a mathematical representation of a system or process that learns from data to make predictions or decisions.
Model is used as a very broad, general term for a variety of very different representations and learning systems. In statistics and machine learning, a model is a simplified representation of a complex system or process that is used to make predictions or draw conclusions about the system or process. A model can be thought of as a mathematical formula or algorithm that captures the relationships between various variables or inputs in the system.
In statistical modeling, models are used to estimate the relationship between a dependent variable and one or more independent variables. For example, a regression model might be used to predict the price of a house based on its size, number of bedrooms, and other factors.
In machine learning, algorithms are used to learn models that capture patterns in data, and make predictions or decisions based on new data. Machine learning models can be trained on large datasets to identify complex relationships between variables and make accurate predictions. For example, a neural network might be trained to classify images of cats and dogs based on features such as fur color and shape.
Both statistical and machine learning models can be used for a variety of purposes, such as prediction, classification, clustering, and optimization. The choice of model depends on the problem at hand, the available data, and the desired outcome.
In Causal Wizard, models are trained for you as part of the estimation, validation and refutation processes. Afterwards, you'll be shown results which describe the behaviour and performance of these models and the implications and conclusions you can draw.