Has ML been used for feature selection in a car classification task?

Yes, in car classification tasks, machine learning (ML) methods are often used for feature selection. This helps identify the most informative characteristics that influence classification and improve the model quality.

Examples of approaches:

• Filter methods: calculating the correlation of features with the target variable, selecting based on statistical criteria.
• Wrapper methods: using models (e.g., decision trees) to evaluate feature importance.
• Embedded methods: algorithms that select features during training (e.g., Lasso, Random Forest).

Thus, ML helps automate and optimize feature selection, which is especially useful when dealing with a large number of car parameters (power, engine type, year of manufacture, etc.).