python-scikit-learnUsing quadratic linear regression
Quadratic regression can be achieved by using PolynomialFeatures to prepare dataset for polynomial form:
from sklearn import preprocessing, linear_model
import numpy as np
data = np.array([[1, 1], [2, 4], [3, 9], [4, 15], [5, 21], [6, 36]])
X = np.array(data[:,0]).reshape(-1,1)
y = data[:,1]
poly = preprocessing.PolynomialFeatures(degree=2, include_bias=False)
Xp = poly.fit_transform(X)
model = linear_model.LinearRegression()
model.fit(Xp, y)
y_pred = model.predict(Xp)ctrl + cfrom sklearn importimport module from scikit-learn | .PolynomialFeatures(create polynomial object to prepare our dataset for polynomial form |
poly.fit_transform(transform original dataset to polynomial form | linear_model.LinearRegressioninitialize linear regression model |
.fit(train transformation model | .predict(predict target variable based on given features dataset |
Usage example
from sklearn import preprocessing, linear_model
import numpy as np
data = np.array([[1, 1], [2, 4], [3, 9], [4, 15], [5, 21], [6, 36]])
X = np.array(data[:,0]).reshape(-1,1)
y = data[:,1]
poly = preprocessing.PolynomialFeatures(degree=2, include_bias=False)
Xp = poly.fit_transform(X)
model = linear_model.LinearRegression()
model.fit(Xp, y)
print(model.predict(Xp))output
[ 1.57142857 3.62857143 7.97142857 14.6 23.51428571 34.71428571]
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