Pearson correlation
The usual definition of correlation. Technically,
Corr[X,Y] = Cov[X,Y]/(Std[X] * Std[Y]).Pearson correlation
The usual definition of correlation. Technically,
Corr[X,Y] = Cov[X,Y]/(Std[X] * Std[Y]).correlation
Two ways: First we can scan for correlations,
and see the list of values, which can be correlated.
From this we can pick a value to be correlated.
Secondly, we can record two scripts and
compare them. We can look up the difference
file to see for the values, which needed
to be correlated. In my project,
there was a unique id developed for each customer,
it was nothing but Insurance Number,
it was generated automatically and it was
sequential and this value was unique.
I had to correlate this value, in order
to avoid errors while running my script.
I did using scan for correlation.correlation meaning
In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. In the broadest sense correlation is any statistical association, though it commonly refers to the degree to which a pair of variables are linearly related.correlation mlib
import org.apache.spark.ml.linalg.{Matrix, Vectors}
import org.apache.spark.ml.stat.Correlation
import org.apache.spark.sql.Row
val data = Seq(
Vectors.sparse(4, Seq((0, 1.0), (3, -2.0))),
Vectors.dense(4.0, 5.0, 0.0, 3.0),
Vectors.dense(6.0, 7.0, 0.0, 8.0),
Vectors.sparse(4, Seq((0, 9.0), (3, 1.0)))
)
val df = data.map(Tuple1.apply).toDF("features")
val Row(coeff1: Matrix) = Correlation.corr(df, "features").head
println("Pearson correlation matrix:\n" + coeff1.toString)
val Row(coeff2: Matrix) = Correlation.corr(df, "features", "spearman").head
println("Spearman correlation matrix:\n" + coeff2.toString)Copyright © 2021 Codeinu
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