Data Preprocessing Questions
The common techniques used for feature selection in data preprocessing are:
1. Filter methods: These methods use statistical measures to rank the features based on their relevance to the target variable. Examples include correlation coefficient, chi-square test, and information gain.
2. Wrapper methods: These methods involve training a machine learning model with different subsets of features and evaluating their performance. Examples include forward selection, backward elimination, and recursive feature elimination.
3. Embedded methods: These methods incorporate feature selection within the model training process. Examples include LASSO (Least Absolute Shrinkage and Selection Operator) and Ridge regression.
4. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms the original features into a new set of uncorrelated variables called principal components. These components capture the maximum variance in the data and can be used as features.
5. Stepwise Regression: This technique combines forward and backward selection methods to iteratively add or remove features based on their statistical significance.
6. Genetic algorithms: These algorithms use evolutionary principles to search for an optimal subset of features. They evaluate different combinations of features and select the ones that maximize the performance of the model.
It is important to note that the choice of feature selection technique depends on the specific problem, dataset, and the goals of the analysis.