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What is feature reduction in machine learning?
During machine learning, feature reduction removes multicollinearity resulting in improvement of the machine learning model in use. Feature reduction is used to decrease the number of dimensions, making the data less sparse and more statistically significant for machine learning applications.
What is feature reduction in ML?
Dimensionality reduction is simply, the process of reducing the dimension of your feature set. Your feature set could be a dataset with a hundred columns (i.e features) or it could be an array of points that make up a large sphere in the three-dimensional space.
How is dimensionality reduction different from feature selection?
While both methods are used for reducing the number of features in a dataset, there is an important difference. Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension.
What is the difference between feature selection and feature extraction?
Feature selection is about choosing some of features based on some statistical score but feature extraction is using techniques to extract some second layer information from the data e.g. interesting frequencies of a signal using Fourier transform.
How is feature selection used in model construction?
According to wikipedia, “feature selection is the process of selecting a subset of relevant features for use in model construction” or in other words, the selection of the most important features. In normal circumstances, domain knowledge plays an important role and we could select features we feel would be the most important.
How is feature selection used in machine learning?
Here we will apply PCA and keep 90% of the variance: We can see that we are left with 139 features that explain 90% of the variance in our data. Feature selection is an important part of any machine learning process.