from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(X_train, y_train) With Scikit-Learn it is extremely straight forward to implement linear regression models, as all you really need to do is import the LinearRegression class, instantiate it, and call the fit() method along with our training data. from sklearn.linear_model import LinearRegression regressor=LinearRegression() regressor.fit(X_train,y_train) Here LinearRegression is a class and regressor is the object of the class LinearRegression.And fit is method to fit our linear regression model to our training datset. Linear regression is one of the most popular and fundamental machine learning algorithm. can be negative (because the model can be arbitrarily worse). If fit_intercept = False, this parameter will be ignored. # Linear Regression without GridSearch: from sklearn.linear_model import LinearRegression: from sklearn.model_selection import train_test_split: from sklearn.model_selection import cross_val_score, cross_val_predict: from sklearn import metrics: X = [[Some data frame of predictors]] y = target.values (series) In this post, we’ll be exploring Linear Regression using scikit-learn in python. LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation. If we draw this relationship in a two-dimensional space (between two variables), we get a straight line. parameters of the form
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