🔍Filter methods focus on specific traits of each feature using statistical tests.
🧩Wrapper methods use predictive models to evaluate subsets of features.
🍴Embedded methods simultaneously build the model and perform feature selection.
⏰Filter methods are computationally cheaper and easily scalable to high dimensional datasets.
⚖️Wrapper methods offer a comprehensive search of feature set space but have high computational cost and risk of overfitting.
What are filter methods?
—Filter methods focus on specific traits of each feature using statistical tests and measures to identify relevant features.
How do wrapper methods work?
—Wrapper methods use predictive models to train with different combinations of features and select the set that makes the most accurate model.
What are embedded methods?
—Embedded methods build the model and perform feature selection simultaneously, incorporating feature selection within the model training process.
When should I use filter methods?
—Filter methods are useful when dealing with higher dimensional data and as a preliminary filter before using more intricate methods.
What are the advantages of wrapper methods?
—Wrapper methods interact with the classifier, offer a comprehensive search of feature set space, and consider feature dependencies.
00:00Introduction to the different methods for feature selection: filter methods, wrapper methods, and embedded methods.
00:27Filter methods focus on specific traits of each feature using statistical tests and measures for quick elimination of irrelevant features.
01:25Wrapper methods train predictive models with different combinations of features to select the set that makes the most accurate model.
02:19Embedded methods build the model and perform feature selection simultaneously, incorporating feature selection within the model training process.
03:11Advantages and disadvantages of filter methods: computationally cheaper, fast running time, but no interaction with classification models and ignores feature dependencies.
03:51Advantages and disadvantages of wrapper methods: interaction with the classifier, comprehensive search of feature set space, but high computational cost and risk of overfitting.
04:33Advantages and disadvantages of embedded methods: computationally less expensive, faster running time, interactions with the classification model, and lower risk of overfitting.
04:58Conclusion and closing remarks: understanding the differences and uses of filter methods, wrapper methods, and embedded methods.
