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Linear Regression in Scikit Learn

Linear Regression in Scikit Learn

Scikit Learn provides an easy-to-use implementation of Linear Regression. You can discover more about that implementation here.

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Linear Regression in Scikit Learn

We will be looking into how to use Scikit Learn, the famous library for classical Machine Learning, for Linear Regression.

Implementation 1

The following code snippet illustrates how LinearRegression() class is used in the implementation.

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import pandas as pd 
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
dataset = pd.read_csv("/usr/local/notebooks/datasets/tips.csv")
X = dataset[["total_bill"]]
y = dataset[["tip"]]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, train_size=0.7)
reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)
print("The MSE on test set is {0:.4f}".format(mean_squared_error(y_test, y_pred)))

  • Line 1-4 imports the necessary modules.

  • Line 6 loads the dataset from the github repository. ...