Prediction in machine learning refers to the process of using a trained model to make predictions on new, unseen data. Given a set of input features, the model generates a prediction for the target variable based on the relationships it learned during the training process.
The data type of the features can vary, but it's important to ensure that the data is in a format that can be processed by the model. Some common data types for features include:
Numeric: Numeric data can be continuous or discrete and is used to represent quantities.
Categorical: Categorical data represents categories or groups.
Binary: Binary data consists of two categories, usually represented by 0 and 1.
Ordinal: Ordinal data represents categories with a natural order.
Datetime: It refers to a specific data type used to store date and time information. Although datetime types can be stored in flexible ways, you need to make sure that the dates in your data have a valid format.
Text: Text data is unstructured and requires pre-processing to convert it into a format that can be used by the model.
In addition to the data type, it's also important to consider the quality of the data and ensure that it is free from missing values, outliers, and other errors that can affect the performance of the model.
Updated on: 31/01/2023