python-scikit-learnLinear regression with multiple variables
In order to use multiple feature variables with linear regression we can just use 2-dimensional arrays as in below X variable example:
from sklearn import linear_model
X = [[1,2], [2,1], [2,3], [3,2], [4,5]]
y = [3, 3, 5, 6, 9]
model = linear_model.LinearRegression()
model.fit(X, y)
r2 = model.score(X,y)ctrl + cfrom sklearn importimport module from scikit-learn | linear_model.LinearRegressioninitialize linear regression model |
X = declare feature dataset with 2 variables and 5 objects | [1,2]first object variables (we have 2, but it can have any number of variables) |
.fit(train model with a given features and target variable dataset | scoretrained model |
Usage example
from sklearn import linear_model
X = [[1,2], [2,1], [2,3], [3,2], [4,5]]
y = [3, 3, 5, 6, 9]
model = linear_model.LinearRegression()
model.fit(X, y)
r2 = model.score(X,y)
print(r2)output
0.981789802289282
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