Cannot find the degree that fits my polynomial regression model in sklearn
I have a polynomial features function that I want to give a degree of (1/2) or 0.5, because the data set used is a downward plateau with a degree of at most (1/2), however the predictions produced are all the same and the r-squared mean is -23736.436220427375. When the degree is changed to 2 and onward the predictions increase as the X values get past the mid-point, resulting in a parabola, where as the data is not a parabola.
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
df = pd.read_csv('infantmortality.csv',sep=',')
x = df['Year']
y = df['Infant Mortality Rate']
x_Train = np.array(x[96:150]).reshape(-1, 1)
y_Train = y[96:150]
x_Test = np.array(x[150:193]).reshape(-1, 1)
y_Test = x[150:193]
poly = PolynomialFeatures(degree=int(2))
X_ = poly.fit_transform(x_Train)
x_test_ = poly.fit_transform(x_Test)
lg = LinearRegression(fit_intercept=False, normalize=True)
lg.fit(X_, y_Train)
score = lg.score(x_test_, y_Test)
val = [2005]
val_ = poly.fit_transform(np.array(val).reshape(-1, 1))
print lg.predict(val_)
python scikit-learn regression non-linear-regression
asked Nov 15 '18 at 23:48
Teriq StrachanTeriq Strachan
11
add a comment |
I have a polynomial features function that I want to give a degree of (1/2) or 0.5, because the data set used is a downward plateau with a degree of at most (1/2), however the predictions produced are all the same and the r-squared mean is -23736.436220427375. When the degree is changed to 2 and onward the predictions increase as the X values get past the mid-point, resulting in a parabola, where as the data is not a parabola.
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
df = pd.read_csv('infantmortality.csv',sep=',')
x = df['Year']
y = df['Infant Mortality Rate']
x_Train = np.array(x[96:150]).reshape(-1, 1)
y_Train = y[96:150]
x_Test = np.array(x[150:193]).reshape(-1, 1)
y_Test = x[150:193]
poly = PolynomialFeatures(degree=int(2))
X_ = poly.fit_transform(x_Train)
x_test_ = poly.fit_transform(x_Test)
lg = LinearRegression(fit_intercept=False, normalize=True)
lg.fit(X_, y_Train)
score = lg.score(x_test_, y_Test)
val = [2005]
val_ = poly.fit_transform(np.array(val).reshape(-1, 1))
print lg.predict(val_)
python scikit-learn regression non-linear-regression
asked Nov 15 '18 at 23:48
Teriq StrachanTeriq Strachan
11
add a comment |
I have a polynomial features function that I want to give a degree of (1/2) or 0.5, because the data set used is a downward plateau with a degree of at most (1/2), however the predictions produced are all the same and the r-squared mean is -23736.436220427375. When the degree is changed to 2 and onward the predictions increase as the X values get past the mid-point, resulting in a parabola, where as the data is not a parabola.
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
df = pd.read_csv('infantmortality.csv',sep=',')
x = df['Year']
y = df['Infant Mortality Rate']
x_Train = np.array(x[96:150]).reshape(-1, 1)
y_Train = y[96:150]
x_Test = np.array(x[150:193]).reshape(-1, 1)
y_Test = x[150:193]
poly = PolynomialFeatures(degree=int(2))
X_ = poly.fit_transform(x_Train)
x_test_ = poly.fit_transform(x_Test)
lg = LinearRegression(fit_intercept=False, normalize=True)
lg.fit(X_, y_Train)
score = lg.score(x_test_, y_Test)
val = [2005]
val_ = poly.fit_transform(np.array(val).reshape(-1, 1))
print lg.predict(val_)
python scikit-learn regression non-linear-regression
asked Nov 15 '18 at 23:48
Teriq StrachanTeriq Strachan
11
I have a polynomial features function that I want to give a degree of (1/2) or 0.5, because the data set used is a downward plateau with a degree of at most (1/2), however the predictions produced are all the same and the r-squared mean is -23736.436220427375. When the degree is changed to 2 and onward the predictions increase as the X values get past the mid-point, resulting in a parabola, where as the data is not a parabola.
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
df = pd.read_csv('infantmortality.csv',sep=',')
x = df['Year']
y = df['Infant Mortality Rate']
x_Train = np.array(x[96:150]).reshape(-1, 1)
y_Train = y[96:150]
x_Test = np.array(x[150:193]).reshape(-1, 1)
y_Test = x[150:193]
poly = PolynomialFeatures(degree=int(2))
X_ = poly.fit_transform(x_Train)
x_test_ = poly.fit_transform(x_Test)
lg = LinearRegression(fit_intercept=False, normalize=True)
lg.fit(X_, y_Train)
score = lg.score(x_test_, y_Test)
val = [2005]
val_ = poly.fit_transform(np.array(val).reshape(-1, 1))
print lg.predict(val_)
python scikit-learn regression non-linear-regression
python scikit-learn regression non-linear-regression
asked Nov 15 '18 at 23:48
Teriq StrachanTeriq Strachan
11
asked Nov 15 '18 at 23:48
Teriq StrachanTeriq Strachan
11
asked Nov 15 '18 at 23:48
Teriq StrachanTeriq Strachan
11
asked Nov 15 '18 at 23:48
Teriq StrachanTeriq Strachan
11
asked Nov 15 '18 at 23:48
Teriq StrachanTeriq Strachan
11
11
add a comment |
add a comment |
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