I'm trying to implement the Random Forest algorithm for Classification and Regression problems, both from scratch and by using the already available sklearn packages in python.
Tried to understand how Decision trees work.
Books:
Hand-On Machine Learning with SciKit-Learn, Keras and Tensorflow & Data Science from Scratch by Joel Grus.
Tried to understand concepts like Gini index, entropy, information gain and pruning of Decision trees.
Article link: https://towardsdatascience.com/gini-index-vs-information-entropy-7a7e4fed3fcb
Understood what Bootstrap Aggregation (Bagging) is and how that along with feature randomness is used to implement Random forest algorithms.
Article link: https://machinelearningmastery.com/bagging-and-random-forest-ensemble-algorithms-for-machine-learning/
Added DecisionTree.py.