![]() MultiOutputRegressor: to create a multioutput regressor.train_test_split: to split the data into train and test. ![]() make_regression: to create a regression dataset.There are few packages that we would be loading here Import packages from sklearn.datasets import make_regressionįrom sklearn.model_selection import train_test_splitįrom sklearn.multioutput import MultiOutputRegressorįrom sklearn.ensemble import RandomForestRegressor In the next couple of sections, let me walk you through, how to solve multi-output regression problems using sklearn. Some applications for multi-output target variable problems are in forecasting and predictive maintenance. In my professional experience, I see about 90% of the data science regression problems usually have a single target variable and the rest usually require fitting for multiple target variables. In classification, the categorical target variables are encoded to convert them to multi-output. Multi-output machine learning problems are more common in classification than regression. That’s right! there can be more than one target variable. Regression analysis is a process of building a linear or non-linear fit for one or more continuous target variables.
0 Comments
Leave a Reply. |