module metrics.correlations¶

Short summary¶

module mlinsights.metrics.correlations

Correlations.

source on GitHub

Functions¶

function

truncated documentation

non_linear_correlations

Computes non linear correlations.

Documentation¶

Correlations.

source on GitHub

mlinsights.metrics.correlations.non_linear_correlations(df, model, draws=5, minmax=False)[source]

Computes non linear correlations.

Parameters
• model – machine learned model used to compute the correlations

• draws – number of tries for bootstrap, the correlation is the average of the results obtained at each draw

• minmax – if True, returns three matrices correlations, min, max, only the correlation matrix if False

Returns

see parameter minmax If variables are centered, , it becomes: If rescaled, , then it becomes . Let’s assume we try to find a coefficient such as minimizes the standard deviation of noise : It is like if coefficient comes from a a linear regression which minimizes . If variable , are centered and rescaled: . We extend that definition to function of parameter defined as: . is not linear anymore. Let’s assume parameter minimizes quantity . Then and we choose such as . Let’s define a non linear correlation bounded by as: We can verify that this value is in interval:math:[0,1]. That also means that there is no negative correlation. is a machine learned model and most of them usually overfit the data. The database is split into two parts, one is used to train the model, the other one to compute the correlation. The same split are used for every coefficient. The returned matrix is not necessarily symmetric.

Compute non linear correlations

The following example compute non linear correlations on Iris datasets based on a RandomForestRegressor model.

<<<

import pandas
from sklearn import datasets
from sklearn.ensemble import RandomForestRegressor
from mlinsights.metrics import non_linear_correlations

X = iris.data[:, :4]
df = pandas.DataFrame(X)
df.columns = ["X1", "X2", "X3", "X4"]
cor = non_linear_correlations(df, RandomForestRegressor())
print(cor)


>>>

              X1        X2        X3        X4
X1  0.998721  0.000000  0.849184  0.782002
X2  0.000000  0.995516  0.275266  0.032581
X3  0.879026  0.589137  0.999289  0.962060
X4  0.745927  0.673385  0.968552  0.999138


source on GitHub