How to Use Tree-Based Algorithms for Machine Learning

Tree-based algorithms are popular machine learning methods used to solve supervised learning problems. These algorithms are flexible and can solve any kind of problem at hand (classification or regression).

Tree-based algorithms tend to use the mean for continuous features or mode for categorical features when making predictions on training samples in the regions they belong to. They also produce predictions with high accuracy, stability, and ease of interpretation.

Examples of Tree-based Algorithms

There are different tree-based algorithms that you can use, such as

  • Decision Trees
  • Random Forest
  • Gradient Boosting
  • Bagging (Bootstrap Aggregation)

So every data scientist should learn these algorithms and use them in their machine learning projects.

In this article, you will learn more about the Random forest algorithm. After completing this article, you should be proficient at using the random forest algorithm to solve and build predictive models for classification problems with scikit-learn.

What is Random Forest?

Random forest is one of the most popular tree-based supervised learning algorithms. It is also the most flexible and easy to use.

The algorithm can be used to solve both classification and regression problems. Random forest tends to combine hundreds ofdecision treesand then trains each decision tree on a different sample of the observations.

The final predictions of the random forest are made by averaging the predictions of each individual tree.

The benefits of random forests are numerous. The individual decision trees tend to overfit to the training data but random forest can mitigate that issue by averaging the prediction results from different trees. This gives random forests a higher predictive accuracy than a single decision tree.

The random forest algorithm can also help you to find features that are important in your dataset. It lies at the base of the Boruta algorithm, which selects important features in a dataset.

Random forest has been used in a variety of applications, for example to provide recommendations of different products to customers in e-commerce.

In medicine, a random forest algorithm can be used to identify the patient’s disease by analyzing the patient’s medical record.

Also in the banking sector, it can be used to easily determine whether the customer is fraudulent or legitimate.

How does the Random Forest algorithm work?

The random forest algorithm works by completing the following steps:

Step 1: The algorithm select random samples from the dataset provided.

Step 2: The algorithm will create a decision tree for each sample selected. Then it will get a prediction result from each decision tree created.

Step 3: Voting will then be performed for every predicted result. For a classification problem, it will use mode, and for a regression problem, it will use mean.

Step 4: And finally, the algorithm will select the most voted prediction result as the final prediction.

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How to Use Tree-Based Algorithms for Machine Learning
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sophia tondon

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Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

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When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
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  • Anaconda 4.2 – Anaconda is an opensource Python package

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Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
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In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

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Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
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  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
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Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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