Random forest is a popular regression and classification algorithm. In this tutorial we will see how it works for classification problem in machine learning. It uses decision tree underneath and forms multiple trees and eventually takes majority vote out of it. We will go over some theory first and then solve digits classification problem using sklearn RandomForestClassifier. In the end we have an exercise for you to solve.

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Code: https://github.com/codebasics/py/blob/master/ML/11_random_forest/11_random_forest.ipynb

Exercise: Exercise description is avialable in above notebook towards the end

Exercise solution: https://github.com/codebasics/py/blob/master/ML/11_random_forest/Exercise/random_forest_exercise.ipynb

Topics that are covered in this Video:
0:00 Random forest algorithm
0:50 How to build multiple decision trees based on single data set?
2:34 Use of sklearn digits data set to make a classification using random forest
3:04 Coding (Start) (Use sklearn digits dataset for classification using random forest)
7:10 sklearn.ensemble RandomForestClassifier
10:36 Confusion Matrix (sklearn.metrics confusion_matrix)
12:04 Exercise (Classify iris flower using sklearn iris flower dataset and random forest classifier)

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Machine Learning Tutorial Python 12 – K Fold Cross Validation: https://www.youtube.com/watch?v=gJo0uNL-5Qw&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=13

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