The correct answer is Feature Selection.
Feature selection is the process of selecting a subset of features from a set of features for use in machine learning. The goal of feature selection is to improve the performance of the machine learning model by reducing the dimensionality of the data while preserving as much information as possible.
There are many different feature selection methods, and the best method to use depends on the specific machine learning problem. Some common feature selection methods include:
- Correlation-based feature selection: This method selects features that are highly correlated with the target variable.
- Recursive feature elimination: This method starts with all features and then iteratively removes features that do not improve the performance of the machine learning model.
- Genetic algorithm-based feature selection: This method uses a genetic algorithm to select features.
Data reduction is the process of reducing the amount of data without losing important information. This can be done by removing duplicate data, aggregating data, or using dimensionality reduction techniques.
Data encoding is the process of converting data into a form that can be used by a machine learning algorithm. This can be done by converting text data into numerical data, or by converting categorical data into numerical data.
Data normalization is the process of transforming data so that it has a normal distribution. This can be done by subtracting the mean from each data point and then dividing by the standard deviation.