https://github.com/geekquad/random-forest-from-scratch

A basic implementation of the Random Forest Classifier from Scratch and using Seaborn to find important features.
https://github.com/geekquad/random-forest-from-scratch

iris-dataset random-forest random-forest-classifier seaborn sklearn

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A basic implementation of the Random Forest Classifier from Scratch and using Seaborn to find important features.

• Host: GitHub
• URL: https://github.com/geekquad/random-forest-from-scratch
• Owner: geekquad
• Created: 2020-06-27T18:51:02.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-06-28T20:39:50.000Z (almost 6 years ago)
• Last Synced: 2025-01-07T05:16:29.349Z (over 1 year ago)
• Topics: iris-dataset, random-forest, random-forest-classifier, seaborn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 49.8 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Random-Forest-from-Scratch

Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. Random forests creates decision trees on randomly selected data samples, gets prediction from each tree and selects the best solution by means of voting. It also provides a pretty good indicator of the feature importance.

## Working of the Algorithm

It works in four steps:

- Select random samples from a given dataset.

- Construct a decision tree for each sample and get a prediction result from each decision tree.

- Perform a vote for each predicted result.

- Select the prediction result with the most votes as the final prediction.



 


## Documentation of Random Forest:

 https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html 

 


## Parameters of the Algorithm:



  
min_samples_split (default value = 2)

  nodes cannot be further seperated below this value. 

  
 criterion : optional (default=”gini”)  

  It controls how a Decision Tree decides where to split the data. 

  It is the measure of impurity in a bunch of examples. 

   This parameter allows us to use the different-different attribute selection measure. Supported criteria are “gini” for the Gini index and “entropy” for the information gain.

  
  n_estimators(default value = 100 



  This parameter tells is the number of trees in the forest.

  

 
   


## Evaluation of the Algorithm:

### a) Without Parameter Tuning:

             precision    recall  f1-score   support

          0       1.00      1.00      1.00        14

          1       0.94      0.94      0.94        17

          2       0.93      0.93      0.93        14

    avg / total   0.96      0.96      0.96        45

#### Accuracy: 0.9555555555555556

### b) After Parameter Tuning:

            precision    recall  f1-score   support

          0       1.00      1.00      1.00        14

          1       1.00      0.94      0.97        17

          2       0.93      1.00      0.97        14

    avg / total   0.98      0.98      0.98        45

#### Accuracy: 0.9777777777777777

 


## Finding Imoprtant Features using Seaborn Library:

Finding important features or selecting features in the IRIS dataset.