Regression
Predicting a continuous numerical value based on input features.
In-depth explanation
Regression is a supervised learning task where the output is a continuous number rather than a discrete category. Linear regression fits a straight line, while more complex methods like polynomial regression, random forest regression, and neural networks can capture non-linear relationships. Evaluation metrics include MSE, RMSE, and R-squared.
Examples
More in Machine Learning
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
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