उचित principal components की संख्या चुनना

R में Marketing Analytics के लिए Machine Learning

Verena Pflieger

Data Scientist at INWT Statistics

प्रासंगिक components की संख्या: explained variance

# Proportion of variance explained:
summary(pcaCust)

Importance of components:
                          PC1    PC2    PC3     PC4     PC5     PC6     PC7
Standard deviation     2.0951 1.8379 1.2960 1.20415 1.12301 1.07453 0.80486
Proportion of Variance 0.2743 0.2111 0.1050 0.09062 0.07882 0.07216 0.04049
Cumulative Proportion  0.2743 0.4855 0.5904 0.68106 0.75989 0.83205 0.87254
                           PC8     PC9    PC10    PC11    PC12    PC13
Standard deviation     0.78236 0.72452 0.61302 0.48428 0.36803 0.25901
Proportion of Variance 0.03826 0.03281 0.02349 0.01466 0.00847 0.00419
Cumulative Proportion  0.91079 0.94360 0.96709 0.98175 0.99021 0.99440
                          PC14    PC15    PC16
Standard deviation     0.20699 0.17126 0.13170
Proportion of Variance 0.00268 0.00183 0.00108
Cumulative Proportion  0.99708 0.99892 1.00000

प्रासंगिक components की संख्या: Kaiser-Guttman मानदंड

Kaiser-Guttman मानदंड: Eigenvalue > 1

pcaCust$sdev ^ 2

 [1] 4.38961593 3.37778445 1.67965616 1.44997580 1.26115351 1.15461579
 [7] 0.64780486 0.61209376 0.52492468 0.37579685 0.23452736 0.13544710
[13] 0.06708362 0.04284504 0.02933027 0.01734481

प्रासंगिक components की संख्या: screeplot

Screeplot या: "elbow ढूँढें"

screeplot(pcaCust, type = "lines")
box()
abline(h = 1, lty = 2)

मानदंड अनुसार सुझाए गए components की संख्या

Explained Variance	Kaiser-Guttman	Screeplot
5	6	6

Biplot

biplot(pcaCust, choices = 1:2, cex = 0.7)

अभ्यास करते हैं!

R में Marketing Analytics के लिए Machine Learning