Skip to main content

Featured Post

Top 10 Advance Java Interview questions?

Top 10 Advance Java Interview questions?   What are the differences between abstract classes and interfaces in Java? What is the difference between ArrayList and LinkedList in Java? What is the purpose of the finalize() method in Java? What is polymorphism in Java and how is it achieved? What are the different types of inner classes in Java? What is the difference between static and non-static methods in Java? What are the different types of exceptions in Java and how do they differ? What is the difference between checked and unchecked exceptions in Java? How does Java handle multithreading and synchronization? What are the different types of JDBC drivers in Java and how do they differ?
Post a Comment

Random Forest Regression in Python

 

Random Forest Regression in Python

Every decision tree has high variance, but when we combine all of them together in parallel then the resultant variance is low as each decision tree gets perfectly trained on that particular sample data and hence the output doesn’t depend on one decision tree but multiple decision trees. In the case of a classification problem, the final output is taken by using the majority voting classifier. In the case of a regression problem, the final output is the mean of all the outputs. This part is Aggregation.

A Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as bagging. The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees.
Random Forest has multiple decision trees as base learning models. We randomly perform row sampling and feature sampling from the dataset forming sample datasets for every model. This part is called Bootstrap.

We need to approach the Random Forest regression technique like any other machine learning technique

  • Design a specific question or data and get the source to determine the required data.
  • Make sure the data is in an accessible format else convert it to the required format.
  • Specify all noticeable anomalies and missing data points that may be required to achieve the required data.
  • Create a machine learning model
  • Set the baseline model that you want to achieve
  • Train the data machine learning model.
  • Provide an insight into the model with test data
  • Now compare the performance metrics of both the test data and the predicted data from the model.
  • If it doesn’t satisfy your expectations, you can try improving your model accordingly or dating your data or use another data modeling technique.
  • At this stage you interpret the data you have gained and report accordingly.
Labels:

Comments

Post a Comment

Popular posts from this blog

WHAT ARE NEURAL NETWORKS? | Comingfly

WHAT ARE NEURAL NETWORKS ? Neural Networks the process of machine learning are neural networks. These are brain-inspired networks of interconnected layers of algorithms, called neurons, that feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to input data as it passes between the layers. During training of these neural networks, the weights attached to different inputs will continue to be varied until the output from the neural network is very close to what is desired, at which point the network will have 'learned' how to carry out a particular task. A subset of machine learning is deep learning, where neural networks are expanded into sprawling networks with a huge number of layers that are trained using massive amounts of data. It is these deep neural networks that have fueled the current leap forward in the ability of computers to carry out task like speech recognition and computer vision. T here are vario...
6 comments
Read more

What is Go Language ? | Comingfly

What is Go Language ? What Go Language? :- v    Go is a modern, general purpose language. v    A new ,Open source Programming language. v    Concurrent , Builds fast at scale. v    Go language is very fast. v    Garbage-Collected. v    Lightweight syntax. v    Simple type system. v    Productive. v    Simple. v    Fun History of Go language :- Go language developed by Rob Pike   and Ken Thompson. More than 250 contributors join the project.   Design began late in 2007.   Become open source in 2009.   Version is 1.2.2. Feature of Go language ? v   In the current world, languages don't help enough. v   Computers fast but software construction slow. v   Efficiency. v   Safety. v   Speed. v   Concurrency. v   Scalability. v   Fast development cycle. v   No surprises. v   Multi-core Why Developed a Language ? Google has bi...
5 comments
Read more

What is Java? | Comingfly

What is Java? Developed by Sun Microsystems in the mid-1990s, Java was originally built to be a high-level and object-oriented programming language that looks and feels similar to C++. Along with being extremely popular, Java can implement a wide variety of algorithms, which are very useful to the machine learning community. Java is regarded as one of the most secure programming languages, largely due to its use of bytecode and sandboxes. Java manages to harness much of the power of C++ while overcoming its security issues and overall complexity. But with all of its improvements over C++, Java has a reputation for being slower than many other programming languages. Additionally, as of 2019, Java has implemented commercial licensing for certain business applications, which can be costly.
Post a Comment
Read more