Skip to main content

Random Forest Algorithm

Random Forest is an ensemble machine learning algorithm that follows the bagging technique. The base estimators in the random forest are decision trees. Random forest randomly selects a set of features that are used to decide the best split at each node of the decision tree.

Looking at it step-by-step, this is what a random forest model does:

1. Random subsets are created from the original dataset (bootstrapping).

2. At each node in the decision tree, only a random set of features are considered to decide the best split.

3. A decision tree model is fitted on each of the subsets.

4. The final prediction is calculated by averaging the predictions from all decision trees.

To sum up, the Random forest randomly selects data points and features and builds multiple trees (Forest).

Random Forest is used for feature importance selection. The attribute (.feature_importances_) is used to find feature importance.

Some Important Parameters:-

1. n_estimators:- It defines the number of decision trees to be created in a random forest.

2. criterion:- "Gini" or "Entropy."

3. min_samples_split:- Used to define the minimum number of samples required in a leaf node before a split is attempted

4. max_features: -It defines the maximum number of features allowed for the split in each decision tree.

5. n_jobs:- The number of jobs to run in parallel for both fit and predict. Always keep (-1) to use all the cores for parallel processing.

Labels:

Comments

Post a Comment

Popular posts from this blog

Data Analytics Interview Questions - Part 1

Q1. Python or R – Which one would you prefer for text analytics? We will prefer Python because of the following reasons: Python  would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools. R  is more suitable for machine learning than just text analysis. Python performs faster for all types of text analytics. Q2. How does data cleaning plays a vital role in the analysis? Data cleaning can help in analysis because: Cleaning data from multiple sources helps to transform it into a format that data analysts or data scientists can work with. Data Cleaning helps to increase the accuracy of the model in machine learning. It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources. It might take up to 80% of the time for just c...
Post a Comment
Read more
Myth about Data Science - A must know for all Data Science enthusiast 1. Only Coder /Programmer can only become a Data Science No, its not correct. People who is having Basic Programming skills like Python/R or atleast who can learn basic programming can come in to this field.Here i wanted to suggest people who is having Engineering background /Software they can choose Python as a programming and The person who wanted to transit their career in to data science field but coming from non Engineering background like Arts,Commerce,Science they can prefer R as a Programming language . Here am not saying for non technical background can not learn python , its bit difficult to understand the basic and algorithm but if they are ready to learn no issues, they can take any of these either Python or R., I have Mentioned while choosing any of these which one is good according to me in another article i.e python, you can refer my article to get better understanding. 2. Data Scientist are ma...
Post a Comment
Read more

Data Science Skills

Below are some of the data science skills that every data scientist must know: 1. Change is the only constant It’s not about “Learning Data Science”, it’s about “improving your Data Science skills! The subjects you are learning currently in Grad School are important because no learning go waste but, the real world practicality is totally different from the theory of the books which is taught for decades. Don’t cramp the information, rather understand the big picture. A report states that 50% of things that you learn today regarding IT will be outdated in 4 years. Technology can become obsolete but, learning can’t be. You should have the attitude of learning, updating your knowledge and focusing on your skills(Get your Basics clear) and not on the information you learn! This will help you to survive in this tough and competitive world (I am not scaring you, I am just asking you to prepare your best! You should start focusing on the below skills for becoming a data scientist –...
Post a Comment
Read more