Multi-output Regression models in scikit-learn Python

by Abdul Rawoof A R Updated: Jul 11, 2023

Solution Kit

Multi-output is a regression model where the target variable is a multi-dimensional array. It is used when many target variables depend on the same input feature set.

Here are some used regression models in sci-kit-learn:

Linear Regression:

Linear regression is a basic regression model. It considers a relationship between the input variables and the target variable.

Ridge Regression:

Ridge regression is a variant of linear regression. It adds a penalty term to the ordinary least squares aim function. It helps to reduce the impact of multicollinearity and can prevent overfitting.

Lasso Regression:

Like ridge regression, Lasso regression adds a penalty term to the aim function. It uses the L1 norm penalty, which produces sparse models by setting the coefficients to zero. This can be useful for feature selection.

ElasticNet Regression:

ElasticNet regression combines the L1 and L2 penalties of lasso and ridge regression. It provides a balance between the two. It can handle situations where there are many correlated predictors.

Polynomial Regression:

Polynomial regression extends linear regression by introducing polynomial terms of the input features. This allows for capturing nonlinear relationships between the features and the target variable.

Multi-target regression via input space expansion treats targets as inputs. This influences the method of all the multioutput regressors. For those who don't, you can use the multi-output regressor, which fits one per target. This includes the popular ML algorithms implemented in the sci-kit-learn library. The random forest regressor will predict values within the observations or closer. It will zero each of the targets.

Since dealing with a multi-output classification problem, we need a specific algorithm. Due to the correlations, we should consider the predicted value. It predicts the next target value. It determines whether to use cross-validated predictions for the previous estimator results.

Some regression machine learning algorithms support many outputs. To train such a model, you should provide input data followed by the output data sequence. There are special workaround models. It can wrap and use those algorithms that do not support predicting many outputs. The first model is trained based on the independent features. Then, the first dependent variable in the sequence of target variables.

They help create a separate model for each output. It also helps create a linear sequence of models, one for each output. It is where the output of each model is dependent upon the output of the previous models. An alternative for the random forest approach is to use Support Vector Regression. It should fit multi-target regression problems.

Here is an example of developing multi-output regression models in sci-kit-learn Python:

Fig: Preview of the output that you will get on running this code from your IDE.

Code

In this solution, we are using scikit-learn and Pandas libraries.

Multiple output regression or classifier with one (or more) parameters with Python

PythonLines of Code : 52License : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries :

import pandas as pd
from sklearn.multioutput import MultiOutputRegressor, RegressorChain
from sklearn.linear_model import LinearRegression


dic = {'par_1': [10, 30, 13, 19, 25, 33, 23],
       'par_2': [1, 3, 1, 2, 3, 3, 2],
       'outcome': [101, 905, 182, 268, 646, 624, 465]}

df = pd.DataFrame(dic)

variables = df.iloc[:,:-1]
results = df.iloc[:,-1]

multi_output_reg = MultiOutputRegressor(LinearRegression())
multi_output_reg.fit(results.values.reshape(-1, 1),variables)

multi_output_reg.predict([[100]])

# array([[12.43124217,  1.12571947]])
# sounds sensible according to the training data

#if input variables needs to be treated as categories,
# go for multiOutputClassifier
from sklearn.multioutput import MultiOutputClassifier
from sklearn.linear_model import LogisticRegression

multi_output_clf = MultiOutputClassifier(LogisticRegression(solver='lbfgs'))
multi_output_clf.fit(results.values.reshape(-1, 1),variables)

multi_output_clf.predict([[100]])

# array([[10,  1]])


dic = {'par_1': [10, 30, 13, 19, 25, 33, 23],
       'par_2': [1, 3, 1, 2, 3, 3, 2],
       'outcome': [0, 1, 1, 1, 1, 1 , 0]}

df = pd.DataFrame(dic)

variables = df.iloc[:,:-1]
results = df.iloc[:,-1]

multi_output_clf = MultiOutputClassifier(LogisticRegression(solver='lbfgs',
                                                            multi_class='ovr'))
multi_output_clf.fit(results.values.reshape(-1, 1),variables)

multi_output_clf.predict([[1]])
# array([[13,  3]])

Instructions

Follow the steps carefully to get the output easily.

Install PyCharm Community Edition on your computer.
Open terminal and install the required libraries with following commands.
Install Scikit-learn - pip install scikit-learn.
Install Pandas - pip install pandas.
Create a new Python file(eg: test.py).
Copy the snippet using the 'copy' button and paste it into that file(Use first 18 lines of code only).
Then add print statement to the end line of the code(refer preview of the output).
Run the file using run button.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for 'multi output regression or classifier python' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

The solution is created in PyCharm 2022.3.3.
The solution is tested on Python 3.9.7.
Scikit-learn version 1.2.2.
Pandas version 2.0.0.

Using this solution, we are able to develop multi output regression models using scikit-learn in Python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to develop multi output regression models using scikit-learn in Python.

Dependent Libraries

scikit-learnby scikit-learn

Python

54584

Version:1.2.2

License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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scikit-learnby scikit-learn

Python 54584 Version:1.2.2 License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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pandasby pandas-dev

Python

38689

Version:v2.0.2

License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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Quality

Security

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pandasby pandas-dev

Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Support

Quality

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You can also search for any dependent libraries on kandi like 'scikit-learn' and 'Pandas'.

FAQ:

1. What is a multi-output regression model? How does it differ from traditional regression?

A multi-output regression model is known as a multi-target or variate regression model. It is a type of regression model that can predict many target variables.

In traditional regression, the goal is to predict a single continuous target variable. It is based on a set of input features. In multi-output regression, the aim is to predict many target variables. It is where each target variable can be continuous or categorical.

The difference between traditional and multi-output regression is in the predicted target variables. Traditional regression focuses on a single target variable. But multi-output regression deals with many target variables. This can be useful in various scenarios where there are many related variables. It needs to be predicted together.

2. How is target classification used in multioutput regressors?

Target classification refers to the problem of categorizing the targets into discrete classes. Multioutput regression models are used when there are many target variables. It helps predict target variables. Each target variable can have continuous or discrete values.

3. What advantages does scikit-learn have for building a multi-output regression model?

Scikit-learn is a popular machine-learning library. It offers several advantages for building multi-output regression models:

Ease of use.
Wide range of algorithms.
Flexibility.
Evaluation and model selection.
Integration with other scikit-learn modules.

4. What is the prediction method of the MultiOutputRegressor class?

The MultiOutputRegressor class is a scikit-learn wrapper. It extends the functionality of a base regression algorithm. It supports multi-output regression tasks. The prediction method of the MultiOutputRegressor class makes predictions for given input samples. The prediction method of MultiOutputRegressor follows the scikit-learn convention for regressor classes.

5. Is there any advantage to using many predictors with multi-output variables?

Yes, there can be advantages to using many predictors with multi-output variables. Here are a few potential advantages:

Improved prediction accuracy.
Ability to capture variable dependencies.
Flexibility in modeling.
Scalability.
Interpretability and explainability.

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Multi-output Regression models in scikit-learn Python

Linear Regression:

Ridge Regression:

Lasso Regression:

ElasticNet Regression:

Polynomial Regression:

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