autoqild.utilities.dimensionality_reduction_techniques¶
This Python module provides a function to create a dimensionality reduction model using various techniques from scikit-learn, such as PCA, LDA, t-SNE, and feature selection methods.
Functions
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Creates a dimensionality reduction model based on the specified technique. |
- autoqild.utilities.dimensionality_reduction_techniques.create_dimensionality_reduction_model(reduction_technique, n_reduced=20)[source]¶
Creates a dimensionality reduction model based on the specified technique.
- Parameters:
reduction_technique (str, optional, default=`select_from_model_rf`) –
Technique to use for feature reduction, provided by scikit-learn. Must be one of:
recursive_feature_elimination_et: Uses ExtraTreesClassifier to recursively remove features and build a model.
recursive_feature_elimination_rf: Uses RandomForestClassifier to recursively remove features and build a model.
select_from_model_et: Meta-transformer for selecting features based on importance weights using ExtraTreesClassifier.
select_from_model_rf: Meta-transformer for selecting features based on importance weights using RandomForestClassifier.
pca: Principal Component Analysis for dimensionality reduction.
lda: Linear Discriminant Analysis for separating classes.
tsne: t-Distributed Stochastic Neighbor Embedding for visualization purposes.
nmf: Non-Negative Matrix Factorization for dimensionality reduction.
n_reduced (int, optional) – The number of components or features to reduce to (default is 20).
- Returns:
selection_model – A dimensionality reduction model corresponding to the specified technique.
- Return type:
Dimensionality reduction Model
- Raises:
ValueError – If the specified reduction technique is not defined in {recursive_feature_elimination_et, nmf recursive_feature_elimination_rf, select_from_model_et, select_from_model_rf, pca, lda, tsne}