Effect of Feature Selection on the Accuracy of Machine Learning Model
Journal Title: International Journal of Multidisciplinary Research and Analysis - Year 2023, Vol 6, Issue 09
Abstract
In real life data science problems, it’s almost rare that all the features in the dataset are useful for building a model. In machine learning, feature selection is the process of selecting a subset of relevant features or attributes for constructing a model. Removing irrelevant and redundant features and, selecting relevant features will improve the accuracy of a machine learning model. Furthermore, adding unnecessary variables to a model increases the overall complexity of the model. Our experiment indicates that the accuracy of a classification model is highly affected by the process of feature selection. We train three algorithms (K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron) by selecting all the features and we got accuracies 49%, 84% and 71% accordingly. After doing some feature selection without any logical changes in models code the accuracy scores jumped to 82%, 86% and 78% accordingly which is quite impressive.
Authors and Affiliations
Asst. Professor Mohammad Salim Hamdard, Asst. Professor Hedayatullah Lodin
Keywords
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- EP ID EP721197
- DOI 10.47191/ijmra/v6-i9-66
- Views 48
- Downloads 0
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