.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "glr_examples/analysis/plot_linear_discriminant_analysis.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_glr_examples_analysis_plot_linear_discriminant_analysis.py: ============================================== Linear discriminant analysis (LDA) ============================================== visualizes the LDA to increase the computational efficiency and reduce the degree of overfitting due to the curse of dimensionality in non-regularized models. .. GENERATED FROM PYTHON SOURCE LINES 10-14 .. code-block:: default # Author: L.Kouadio # Licence: BSD-3 clause .. GENERATED FROM PYTHON SOURCE LINES 15-27 .. code-block:: default from watex.datasets import fetch_data from watex.utils import selectfeatures from watex.exlib.sklearn import SimpleImputer from watex.analysis.decomposition import linear_discriminant_analysis data= fetch_data("bagoue original").get('data=dfy1') # encoded flow y = data.flow ; X= data.drop(columns='flow') # select the numerical features X =selectfeatures(X, include ='number') # imputed the missing data X = SimpleImputer().fit_transform(X) linear_discriminant_analysis (X, y , view =True) .. image-sg:: /glr_examples/analysis/images/sphx_glr_plot_linear_discriminant_analysis_001.png :alt: plot linear discriminant analysis :srcset: /glr_examples/analysis/images/sphx_glr_plot_linear_discriminant_analysis_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none array([[-2.0554e-01, -5.8070e-01], [-8.6472e-01, -8.2076e-01], [ 1.4900e-02, 3.8011e-01], [-6.4002e-01, -1.4575e-02], [ 2.1250e-01, 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