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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.
How can we reduce the number of dimensions?
We can reduce the number of dimensions by dropping some of the derived features. But we don’t lose complete information from the original features: derived features are a linear combination of the original features.
How to check missing values in feature selection?
Checking for missing values is a good first step in any machine learning problem. We can then remove columns exceeding a threshold we define. Unfortunately for our dimensionality reduction efforts, this dataset has zero missing values. In sklearn’s feature selection module we find VarianceThreshold.
How to reduce the impact of feature selection?
Ridge will reduce the impact of features that are not important in predicting the target values. This is done with the help of the hyper parameter alpha (α). If α becomes 1 the model would become LASSO and when α becomes 0 the model will become RIDGE. In order to tune the hyper-parameter alpha (α) cross-validation can be used.
Which is an alternate method to feature selection?
As such, dimensionality reduction is an alternate to feature selection rather than a type of feature selection. We can summarize feature selection as follows. Feature Selection: Select a subset of input features from the dataset.
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 data science?
Univariate Feature Selection is a statistical method used to select the features which have the strongest relationship with our correspondent labels. Using the SelectKBest method we can decide which metrics to use to evaluate our features and the number of K best features we want to keep.