Polynomialle Regression Python

[wpdm_package id=’5649′]

import pandas as pd

df = pd.read_csv("./input2.csv")
df.head()
# Beispiel: Normale, lineare Regression

X = df[["laenge", "weite"]].values
Y = df[["marge"]].values

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, Y, random_state = 42, test_size = 0.25)

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

print(model.score(X_test, y_test))

Ergebnis: 0.915137409704

from sklearn.preprocessing import PolynomialFeatures

PolynomialFeatures?
pf = PolynomialFeatures(degree = 2, include_bias = False)
pf.fit(X_train)

X_train_transformed = pf.transform(X_train)[:, [0, 2]]
X_test_transformed = pf.transform(X_test)[:, [0, 2]]

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train_transformed, y_train)

print(model.score(X_test_transformed, y_test))

Ergebnis: 0.988184054931

print(pf.powers_)
# width ^ 1 * length ^ 0
# width ^ 2 * length ^ 0

Ergebnis:

[[1 0]
[0 1]
[2 0]
[1 1]
[0 2]]

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