Regression
In machine learning, regression problems deal with situations when the label information is continuous. This can be predicting the temperature for tomorrow, the stock price, the salary of a person or the rating of an item on an e-commerce website.
There are many models which can solve the regression problem:
- Ordinary Least Squares (OLS) is the usual linear regression
- Ridge regression and LASSO are the regularized variants of OLS
- Tree-based models such as RandomForest
- Neural networks
Approaching a regression problem is very similar to approaching a classification problem, and the general framework stays the same:
- First, you select an evaluation metric
- Then, you split the data into training and testing
- You train the model on training, tune parameters using cross-validation, and do the final verification using the held out testing set.
Machine learning libraries for regression
We have already discussed many machine learning libraries that can deal with classification problems. Typically,...
