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How to do recursive features elimination in Python using DecisionTreeRegressor

Recursive feature elimination (RFE) is a technique used in machine learning to determine the most important features in a dataset. This is done by iteratively removing the least important feature until a certain number of features is reached.

In Python, one can use the library scikit-learn to perform RFE. The RFE module can be applied to any machine learning model, but for this example, we will be using the DecisionTreeRegressor model.

The first step is to import the required libraries, including scikit-learn and pandas. Next, load the dataset into a pandas dataframe, and split it into training and testing sets.

After that, create an instance of the DecisionTreeRegressor class and define the number of features you want to keep. Then, use the RFE module to select the most important features and fit it to the model.

The RFE module will return an array of feature rankings, which can be used to identify the most important features. These features can then be used to build a new model with improved accuracy and efficiency.

One important thing to keep in mind when using RFE is that it can be computationally expensive, especially when working with large datasets. Therefore, it is important to consider the trade-off between accuracy and computational cost.

In summary, RFE is a great way to improve the performance of machine learning models by identifying and removing unnecessary features. By using the RFE module in scikit-learn and DecisionTreeRegressor, it can be easily implemented in Python and make the model more accurate and efficient.

In this Learn through Codes example, you will learn: How to do recursive features elimination in Python using DecisionTreeRegressor.

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