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Feature selection for Machine Learning The data features used to train the machine learning model have a great impact on the ultimate performance. Irrelevant or partially relevant feature can negatively influence the model. The various automatic feature selection techniques are: Univariate Selection Recursive feature elimination Principal Component Analysis Feature Importance Benefits of feature selection techniques: Reduces overfitting Improves accuracy Reduces training time Univariate Selection: This selection can be used to select the features that have the strongest relationship with the output variable. The example below uses scikit-learn which provides SeleceKBest class that can be used combinely with chi-squared (chi2) statistical test for non-negative features to select 4 of the best features from the dataset(Pima Indian). Recursive Feature Elimination: REF works by recursively removing the attributes a...
Pandas Crash Course Pandas provides data structures and functionality to quickly manipulate and analyze data. We have to understand two data structures in pandas 1. Series 2. DataFrames Series and DataFrames A series is a one-dimensional array where the rows and columns can be labeled. A DataFrame is a two-dimensional array where the rows and columns can be labeled.
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