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Home
Python 3
Feature Importance

Feature Importance

Feature Importance
Get Feature Names
Extract Model Coefficients
Create a DataFrame
Top Words Associated with Fake Reviews
Top Words Associated with Genuine Reviews
Explanation

Feature Importance

One advantage of Logistic Regression is that it assigns a coefficient to each feature (word). Words with larger coefficients have a greater influence on the prediction.

By examining these coefficients, we can identify which words contribute most strongly to classifying reviews as fake or genuine.

Get Feature Names

feature_names = tfidf.get_feature_names_out()

Extract Model Coefficients

coefficients = model.coef_[0]

Create a DataFrame

importance = pd.DataFrame({

"Word": feature_names,

"Coefficient": coefficients

})

Top Words Associated with Fake Reviews

importance.sort_values(

by="Coefficient",

ascending=False

).head(20)

Top Words Associated with Genuine Reviews

importance.sort_values(

by="Coefficient",

ascending=True

).head(20)

Explanation

Positive coefficients indicate words that are more strongly associated with fake reviews, while negative coefficients indicate words more commonly associated with genuine reviews.

Examining feature importance helps us understand how the model makes its predictions and can provide valuable insights into linguistic patterns within the dataset.

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Feature Importance

Contents

Feature Importance

Get Feature Names

Extract Model Coefficients

Create a DataFrame

Top Words Associated with Fake Reviews

Top Words Associated with Genuine Reviews

Explanation

Fake Review Detection System using Machine Learning

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Data Structures and Algorithms Tutorial

Computer Architecture

HTML 5 References Tutorial

Docker Tutorial

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GIT Guide

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Spring Boot Tutorial

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Apache Cordova Tutorial

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