In Supervised Machine Learning (SML) there are always labels associated with certain features. The model is trained, and then it can make predictions on new features. In this way, the model is taught by certain features and targets.
- Feature matrix (X): made of observations or objects (rows) and features (columns).
- Target variable (y): a vector with the target information we want to predict. For each row of X there's a value in y.
The model can be represented as a function g that takes the X matrix as a parameter and tries to predict values as close as possible to y targets. The obtention of the g function is what it is called training.
- Regression: the output is a number (car's price)
- Classification: the output is a category (spam example).
- Binary: there are two categories.
- Multiclass problems: there are more than two categories.
- Ranking: the output is the big scores associated with certain items. It is applied in recommender systems.
In summary, SML is about teaching the model by showing different examples, and the goal is to come up with a function that takes the feature matrix as a parameter and makes predictions as close as possible to the y targets.
|
The notes are written by the community. If you see an error here, please create a PR with a fix. |