Выход Linear regression in scikit-learn Question Title * 1. What parameters from the equation below are learned for a linear regression? b, x W, x W, b x Question Title * 2. What purpose do we need true labels (y) for? To use model for prediction. To validate the model and understand it's performance better. We don't need it at all. To estimate error in model's predictions and thus correctly update parameters for further learning. Question Title * 3. Do we need to scale up/down the target value (label) before training the model? We need to scale it down as otherwise we could see inappropriate results of a loss function and the training would be unstable. We need to scale it up, as the bigger target value is the bigger influence it will have on a model. We need to leave as it is. Question Title * 4. Why it's important to split the data into train and test subsets? We don't need to do it. Because we want to train model on a smaller data subset as it will accelerate the training process. Because to adequately estimate the performance of the model, we need to validate it on unseen before data. Question Title * 5. What is the name of the function from sklearn.model_selection package, that splits the data? train_test_split data_split train_and_test_split test_train_split Question Title * 6. How to fit a linear regression model from sklearn.linear_model package with X features and y labels?model = LinearRegression() train(model,X,y) model.fit(X,y) model.fit(X) model.train(X,y) Question Title * 7. How to predict new instances using the already trained model and features X? model.get_results(X) model.predict(X) model.result(X) predict(model,X) Готово