Neural network regression is a machine learning technique used for solving regression problems. In regression tasks, the goal is to predict a continuous numeric value (e.g., a price, a temperature, a score) based on input data. Neural networks, a type of deep learning model, can be used for regression by learning a mapping from input features to the target output.
Here are the key steps and concepts involved in neural network regression:
1. Data Preparation:
— Collect and preprocess your dataset, which should include input features and corresponding target values (the continuous variable you want to predict).
— Divide the data into training and testing sets to evaluate the model’s performance.