How to create a Regression tree using Shogun?

This recipe helps you to create a regression tree using Shogun.

Recipe Objective

This recipe explains how to make a regression tree using shogun
For more related projects-
Project 1
Project 2

Learn to Implement Customer Churn Prediction Using Machine Learning in Python

Regression Tree

Decision tree learning maps observations about an item by using a decision tree as a predictive model to conclude about the item's target value.
Decision trees can be used as the regression tree, where the predicted outcome can be considered a real number.
Classification And Regression Tree (CART) algorithm is a joint method that we can apply to the regression tree.
In the below example we have applied Classification And Regression Tree (CART) algorithm in a multi-class dataset to predict the labels.

x_train = RealFeatures(feats_train)
x_test = RealFeatures(feats_test)
y_train = MulticlassLabels(labels_train)
y_test = MulticlassLabels(labels_test)

classifier.train(x_train)
predict = classifier.apply_multiclass(x_test)

eval = MulticlassAccuracy()
accuracy = eval.evaluate(predict, y_test)

What Users are saying..

profile image

Ameeruddin Mohammed

ETL (Abintio) developer at IBM
linkedin profile url

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More

Relevant Projects

Build Piecewise and Spline Regression Models in Python
In this Regression Project, you will learn how to build a piecewise and spline regression model from scratch in Python to predict the points scored by a sports team.

Build OCR from Scratch Python using YOLO and Tesseract
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.

PyCaret Project to Build and Deploy an ML App using Streamlit
In this PyCaret Project, you will build a customer segmentation model with PyCaret and deploy the machine learning application using Streamlit.

End-to-End ML Model Monitoring using Airflow and Docker
In this MLOps Project, you will learn to build an end to end pipeline to monitor any changes in the predictive power of model or degradation of data.

Build Regression (Linear,Ridge,Lasso) Models in NumPy Python
In this machine learning regression project, you will learn to build NumPy Regression Models (Linear Regression, Ridge Regression, Lasso Regression) from Scratch.

Build a Multi-Class Classification Model in Python on Saturn Cloud
In this machine learning classification project, you will build a multi-class classification model in Python on Saturn Cloud to predict the license status of a business.

Recommender System Machine Learning Project for Beginners-2
Recommender System Machine Learning Project for Beginners Part 2- Learn how to build a recommender system for market basket analysis using association rule mining.

Learn How to Build a Linear Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple linear regression model in PyTorch to predict the number of days subscribed.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Time Series Analysis with Facebook Prophet Python and Cesium
Time Series Analysis Project - Use the Facebook Prophet and Cesium Open Source Library for Time Series Forecasting in Python