This Tutorial will Explain you that how data can be analyze using Discriminant analysis with the help of Python coding.
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Introduction
- 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
| ANOVA | REGRESSION | DISCRIMINANTANALYSIS | |
| Similarity:- No of Dependent Variable | single | single | single |
| Similarity:- No of Dependent Variable | Multiple | Multiple | Multiple |
| Difference:- Nature of Dependent Variable | Continuous | Continuous | Categorical |
| Difference:- Nature of Independent Variable | Categorical | Continuous | Continuous |
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
Where,
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.
clf=LinearDiscriminantAnalysis()
clf.fit(X,Y)
print(clf.predict(X))
clf.score(X,Y)
Thus you will get Accuracy Score for Prediction accuracy.