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Steps Of Discriminant Analysis In Python

Linear Discriminant Analysis In Python


    This Tutorial will Explain you that how data can be analyze using Discriminant  analysis with the help of Python coding.

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    • Discriminant Analysis ( DA ) is one type of Machine Learning Algorithm to Analyzing   and  prediction of Data.
    • This algorithm is used t Discriminate between two or multiple groups .

    Types of Discriminant Algorithm

    • When there is dependent variable has two group or two categories then it is known as Two-group discriminant analysis.
    • When there is dependent variable has more then two group or  categories then it is known as Multiple  discriminant analysis.

    Similarity and Difference between Anova , Regression and Discriminant Analysis

    Similarity:- No of Dependent Variablesinglesinglesingle
    Similarity:- No of Dependent VariableMultipleMultipleMultiple
    Difference:- Nature of Dependent VariableContinuousContinuousCategorical
    Difference:- Nature of Independent VariableCategoricalContinuousContinuous

    Objectives of Discriminant analysis

    • Development of discriminant functions, or linear combinations of the predictor or independent variables, which will best discriminate between the categories of the criterion or dependent variable (groups).
    • Examination of whether significant differences exist among the groups, in terms of the predictor variables.
    • Determination of which predictor variables contribute to most of the inter group differences.

    DA Model

    The discriminant analysis model involves linear combinations of the following form:

    D = b0 + b1X1 + b2X2 + b3X3 + . . . + bkXk


    D  =  discriminant score

    b ‘s  =  discriminant coefficient or weight

    X ‘s  =  predictor or independent variable

    Deciding factor  Wilks‘λ

    Wilks‘ λ :-  Sometimes also called the U statistic, Wilks’ λ for each predictor is the ratio of the within-group sum of squares to the total sum of squares.  Its value varies between 0 and 1.  Large values of     (near 1) indicate that group means do not seem to be different.  Small values of     (near 0) indicate that the group means seem to be different.

    Basic Python code Step for DA

    import pandas as pd

    import numpy as np

    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

    • Import Above Three packages for Dataset and discriminant analysis algorithm loading.
    • Load the dataset in which you want to perform DA.
    • Descide Dependent and Independent Variable
    • Take Dependent variable on y and Independent on X.





    Thus you will get Accuracy Score  for Prediction accuracy.

    ppt for discriminant analysis

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