What is Regression?
Regression, one of the most common types of machine learning models, estimates the relationships between variables. Whereas classification models identify which category an observation belongs to, regression models estimate a numeric value.
In the context of machine learning and data science, regression specifically refers to the estimation of a continuous dependent variable or response from a list of input variables, or features. There are a variety of regression techniques, ranging from the simplest (linear regression) to complicated statistical classic regression models (Lasso, Elastic Net, etc.), to more complex techniques including gradient boosting and neural networks.
Why is Regression important?
Regression is essential for any machine learning problem that involves continuous numbers, which includes a vast array of real-life applications:
- Financial forecasting, such as estimating housing or stock prices
- Automobile testing
- Weather analysis
- Time series forecasting
- Many more
Regression + DataRobot
Although regression is one of the most common algorithms, a lot of manual work still goes into creating a regression model with traditional data science techniques and tools. The DataRobot platform automates regression analysis for datasets with the touch of a few buttons.
Based on the target variable in the dataset, the DataRobot automated machine learning platform automatically decides whether the task is best suited for regression or classification. It also provides error metrics and parameters critical to regression analysis and visualization tools that help users (and their bosses) understand the model’s outcomes and insights.