Linear regression is a fundamental algorithm for modeling the relationship between input and output variables.
It is used for regression predictive modeling problems where a linear relationship is expected between the input variables and the target variable.
The LinearRegression class in scikit-learn provides a straightforward way to create, train, and use linear regression models.
The key steps in using LinearRegression are:
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
# generate regression dataset
X, y = make_regression(n_samples=100, n_features=2, noise=0.1, random_state=1)
# split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
# fit model
model = LinearRegression()
model.fit(X_train, y_train)
# evaluate model
yhat = model.predict(X_test)
mae = mean_absolute_error(y_test, yhat)
print('MAE: %.3f' % mae)
# make a prediction
row = [[1, 1]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])
Running the example gives an output like:
MAE: 0.107
Predicted: 118.701
First, a synthetic regression dataset is generated using the make_regression() function. This creates a dataset with a specified number of samples (n_samples), input features (n_features), and noise in the output variable (noise). The dataset is split into training and test sets using train_test_split().
Next, a LinearRegression model is instantiated and fit on the training data using the fit() method. The LinearRegression class does not have any key hyperparameters to tune. After fitting, the model can be used to make predictions on new data using the predict() method.
The performance of the model is evaluated by comparing the predictions (yhat) to the actual values (y_test) using a metric like mean absolute error (MAE). A single prediction can be made by passing a new data sample to the predict() method.
This example highlights the simplicity of training and using a linear regression model with scikit-learn. The model can be fit directly on the training data without the need for scaling or normalization. Once fit, the model can be used to make predictions on new data, enabling its use in real-world regression problems where a linear relationship is assumed between the inputs and the target variable.