Code a Random Forest from Scratch
Budget: $10 – $30 USD
Complete the jupyter notebook random forest code for $20 in 8 hours:
https://colab.research.google.com/drive/1DTvDHby73XitZjmV2SFBuht8uvrPryZr?usp=sharing
What you need to implement:
1. Utililty class: implement the functions to compute entropy, information gain, perform splitting, and find the best variable (attribute) and split-point. You can add additional methods for convenience
2. DecisionTree class: implement the learn() method to build your decision tree using the utility functions above.
3. DecisionTree class: implement the classify() method to predict the label of a test record using your decision tree.
4. RandomForestclass:implementthemethods_bootstrapping(),fitting(),voting()and user().
5. get_random_seed(), get_forest_size():implement the functions to return a random seed and forest size (number of decision trees) for your implementation
Do not modify the code, except within the functions.
https://colab.research.google.com/drive/1DTvDHby73XitZjmV2SFBuht8uvrPryZr?usp=sharing
What you need to implement:
1. Utililty class: implement the functions to compute entropy, information gain, perform splitting, and find the best variable (attribute) and split-point. You can add additional methods for convenience
2. DecisionTree class: implement the learn() method to build your decision tree using the utility functions above.
3. DecisionTree class: implement the classify() method to predict the label of a test record using your decision tree.
4. RandomForestclass:implementthemethods_bootstrapping(),fitting(),voting()and user().
5. get_random_seed(), get_forest_size():implement the functions to return a random seed and forest size (number of decision trees) for your implementation
Do not modify the code, except within the functions.