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What is the best feature selection method?

There is no best feature selection method. Just like there is no best set of input variables or best machine learning algorithm. At least not universally. Instead, you must discover what works best for your specific problem using careful systematic experimentation.

What is filter method in feature selection?

Filter Methods are generally used as a preprocessing step. The selection of features is independent of any machine learning algorithms. Instead, features are selected on the basis of their scores in various statistical tests for their correlation with the outcome variable.

What is wrapper method in feature selection?

Wrapper Methods: Definition Wrapper methods work by evaluating a subset of features using a machine learning algorithm that employs a search strategy to look through the space of possible feature subsets, evaluating each subset based on the quality of the performance of a given algorithm.

What is feature selection and filtering?

Feature selection is the process of reducing the number of input variables when developing a predictive model. Filter-based feature selection methods use statistical measures to score the correlation or dependence between input variables that can be filtered to choose the most relevant features.

How do you do feature selection?

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Can decision tree be used for feature selection?

For ensembles of decision trees, feature selection is generally not that important. During the induction of decision trees, the optimal feature is selected to split the data based on metrics like information gain, so if you have some non-informative features, they simply won't be selected.

How do you do feature selection using random forest?

Prepare the dataset. Train a random forest classifier. Identify the most important features. Create a new 'limited featured' dataset containing only those features. Train a second classifier on this new dataset. Compare the accuracy of the 'full featured' classifier to the accuracy of the 'limited featured' classifier.

How is feature importance calculated in random forest?

Feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node. The node probability can be calculated by the number of samples that reach the node, divided by the total number of samples. The higher the value the more important the feature.

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