Most Useful Guide on Feature Selection Python

Managing a large dataset is always a big issue either you are a big data analytics expert or a machine learning expert. But, wait! Have you ever checked how many feature selection Python you are using? 

Sounds strange!!

But, you read it right. The larger the features you use, the more will be the dataset. But, not always! Moreover, it is also observed that the features’ contribution might take you towards less predictive models. 

Below, I have mentioned all the necessary points that help you to understand feature selection Python. So, without creating more suspense, let’s get familiar with the details of feature selection.

What is feature selection?

It is the method that uses to select the most important features from the given dataset. In several cases, it has been noticed that feature selection can improve the performance of the machine learning models. 

We can also say that it is one of the processes to select the most relevant dataset features. 

Moreover, feature selection Python plays an important role in various ways. How? Let’s find it out!

Feature selection allows the use of machine learning algorithms for training the models. That results in less training time.
Feature selection enhances the correctness of the model by selecting the correct subset.
It eliminates overfitting. It means that there is less opportunity to make the decision based on noise.
Feature selection also reduces the model’s complexity that makes it easier to interpret the data.

What are the methods for feature selection Python?

There are various methods that can be used for feature selection. Let’s find out each one in detail.

Filter method

It depends on the data’s uniqueness. Moreover, it involves the same assessment process that includes information, consistency, distance, and dependency. 

The below flow diagram describes the process of the filter method.

Apart from this, the filter method uses the ranking process for variable selection. And the reason for using it is the simplicity, relevancy, and excellence of the rank ordering method. 

Using the filter method, it is possible to eliminate the irrelevant features before starting the classification. 

This method is used for data processing. The feature provides the rank based on the statistics score. This score uses to know the correlation feature with the output variable. 

Some of the examples of filter methods are information gain, Chi-squared test, and correlation coefficient scores.

Wrapper method

It is quite clear that a wrapper method requires a machine learning algorithm. Moreover, the performance of the ML algorithm uses as an evaluation process. 

The accuracy of prediction uses the classification task to evaluate the features. The wrapper method searches the best-fitted feature for the ML algorithm and tries to improve the mining performance.

Some of the wrapper method examples are backward feature elimination, forward feature selection, recursive feature elimination, and much more.

Backward elimination: This process needs the whole set of attributes. 
With every step, backward elimination eliminates the worst attributes and finally includes the best-suited features.
Forward selection: In this process, there is a need for an empty set of features. Once it selects the original features, it adds them to the reduction set. 
With each iteration, the best of the remaining attributes will keep on adding to the existing set.
Recursive feature elimination: In this method, the models keep on creating with the iteration. 
Finally, the worst or best-performing feature determines with each iteration.

Embedded method

This method considers each iteration that is done during the model training process. Moreover, it extracts the features that have contributed the most to the training process. 

The regularization method is a common method used for embedded methods. This applies to finding out the worst feature that yields a coefficient threshold.

Because of this, the regularization method is also known as the penalization method. It also includes additional constraints used for predictive algorithm optimization.

Some examples of regularization algorithms are the Elastic Net, LASSO, Ridge Regression, and much more.

Important things to consider in features selection Python

Now, it is cleared to you that it is worthy of using the feature selection Python method. But still, there is an important point that you have to keep in mind. 

That is where you need to integrate feature selection in the ML pipeline.

If I say simply, the feature selection method should include just before giving the data to the training model. 

In particular, it uses while you are working with the estimation method like cross-validation.

Cross-validation ensures that the feature selection must be performed over the data just before the training of the model. 

NOTE: If you use feature selection to prepare the data first, then the model selection performing and training can be a blunder.

But when you perform feature selection over the whole data, then the cross-validation selects the useful features. This leads to bias in the ML model’s performance.

Now, let’s understand how does feature selection Python work?

Below is the example that uses Recursive feature elimination along with the logistic regression algorithms. 

This algorithm will select the best 3 features from the entire features. 

The selection done by the algorithm does not matter till it is constant and skillful.

It is clear that RFE selects the best 3 features as mass, preg, and Pedi.

Key point: It is important to notice that the result of this code can vary. It produces the results as per the evaluation process. 
That is why it is beneficial to run the example a few times to get the average output of the given code.

The output is marked as choice “1” within the ranking_array and as TRUE within the support_array.

Which feature selection method is best?

It always depends on the user for which purpose they are using these feature selections. 

But still, there are the following points that help you decide which method is best for you.

The filter method seems to be less accurate. But, it works really well while performing the EDA. 
Moreover, the filter method is used to check collinearity among the multiple variables in data.
On the other hand, Embedded and Wrapper methods provide correct or accurate outputs. 
The only drawback to using these methods is that they are quite expensive. 
That is why try to use them when you work with a less number of features (20 features approximately).

Let’s wrap it up!!

Feature selection Python is a method that helps in selecting the features automatically. 

In the above-mentioned process, those features are selected that contribute the most to predicting the output variables that seem interesting to you.

Above, I have mentioned the most useful methods for feature selection. Hope you understand each method’s specialty. 

But, if you have any doubts regarding feature selection Python, comment your query below. I will definitely be going to help you in the best possible way.

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