Why feature selection is important in machine learning? #16
Feature selection means selecting a subset of features from the input data by removing irrelevant or redundant features. This helps to feed only the most relevant features into training a machine learning model.
Some of the reasons why feature selection is an important step in building a machine learning model are given below:
Reduce the curse of dimensionality
Curse of dimensionality means that the amount of data needed for training a model increases with the increase in the dimension of the feature space. So, reducing the dimension of the feature space through feature selection helps to reduce the curse of dimensionality.Reduce the training time
More data we have, more time it will take to train the model. So, reducing the number of features in the data helps to reduce the training time.Reduce the chances of overfitting
If we have large number of features, there are high chances that the model will learn from noise and will lead to overfitting. Keeping only most useful features in the training data reduces the chance of overfitting.Improve the predictive accuracy
Feature selection helps to remove irrelevant features. So, the data is less misleading and it helps to improve the prediction accuracy of the model because the model learns from the most useful information only.Improve the interpretability of results
Having limited features in the model helps to understand the underlying patterns and relationships among features in a better way. This provides a deeper insights into the model performance and results. Too many features make it difficult to interpret the results and performance.
Would you like to add any other reason to the list?
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-Kavita
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