Use Feature Selection Algorithms
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Here we use lasso to select variables.
Use feature selection algorithms. Feature selection algorithms 5 and genetic algorithms. Feature selection is one of the core concepts in machine learning which hugely impacts the performance of your model. It enables the machine learning algorithm to train faster.
The choice of algorithm does not matter too much as long as it is skillful and consistent. As said before embedded methods use algorithms that have built in feature selection methods. The data features that you use to train your machine learning models have a huge influence on the performance you can achieve.
For example lasso and rf have their own feature selection methods. You can see that rfe chose the the top 3 features as preg mass and pedi. By limiting the number of features we use rather than just feeding the model the unmodified data we can often speed up training and improve accuracy or both.
A learning algorithm takes advantage of its own variable selection process and performs feature selection and classification simultaneously such as the frmt algorithm. The example below uses rfe with the logistic regression algorithm to select the top 3 features. It improves the accuracy of a model if the right subset is chosen.
Irr e levant or partially relevant features can negatively impact model performance. Application of feature selection metaheuristics. It reduces the complexity of a model and makes it easier to interpret.
This is a survey of the application of feature selection metaheuristics lately used in the literature. Feature selection methods are intended to reduce the number of input variables to those that are believed to be most useful to a model in order to predict the target variable. The third approach is the.
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