Update README.md

Author: amit-raj-repo

README.md

@@ -1,2 +1,43 @@
-# ML-Random-Forest-Python
-This repository will help in understanding the basic concept of Random Forest algorithm and will also learn how to optimize the hyperparameters and prevent overfitting. 
+# Random Forest
+Random forest is an **Ensemble** machine learning algorithm. Earlier the decision of classification was based on one model, either a logistic or a decision tree, Random Forest uses n decision trees to classify a sample in it's respective class. It uses different parts of data to build trees and each tree has a say in final decision making.
+
+Also, there are a few shortcomings of decision trees:
+* They can fit the data in hand well but when it comes to classifying new data, they do not have enough flexibility to accomodate that
+
+Random Forest combines the simplicity of a decision tree with flexibity to accomodate new data resulting into vast improvement in accuracy.
+
+## Steps to create a Random Forest
+
+* Bootstrapping: We randomly select a sample from our dataset, here one record can be selected multiple times
+* Now we create a decision tree using the bootstrap dataset, say we have 10 independent variables
+  * Here, at each step, we need to randomply select n variables and check which splits the best
+  * Say we have fixed the # of random variables for making decision at each step to 4
+  * For root node, we randomly select 4 out of 10 variables
+  * For each variable, we check which gives the best split, same as decision trees
+  * Say variable 3 gave the best split, then it'll become our root node
+  * The root node splits into 2 nodes, one in right and one in left, now we take the left one
+  * We consider all variables apart from variable 3(as it's already used in root node) and randomly select 4 out of 9
+  * We continue this process until we have a fully grown decision tree
+  * We can choose the number of variables required to analyzed to create a node, it can be any number <= total variables
+  * NOTE- all variables are up for selection in random list apart from the one which has split the node we are working on
+* The above steps are repaeated 100s of times (from bootstrapping to making a tree based on above logic)
+* This complete process results into a wide variety of trees, this variety makes Random Forest more effictive than a decision tree
+
+## Decision Making using Random Forest
+
+* Say we have built a 100 trees using the above algorithm
+* We have got a new sample, we run the new sample through all the trees
+* 75 trees classify the sample as 1 and 25 as 0
+* We will classify the new sample as 1 as it gets majority of votes
+
+This complete process including Bootstrapping the data and using aggregate votes to make a decision is called **Bagging**.
+
+## Measuring How Good a Random Forest is
+
+* Say we had 1000 records in our training dataset
+* When we built the 1st bootstrapped dataset, we got 750 distinct records and since some were chosen multiple times, we had total of 1000 records
+* This means 250 records were not chosen in bootstrapped dataset 1
+* We run these 250 records through all trees and check if they are correctly classified
+* We calculate the error for missclassification
+* Same is done for all the not selected records for each bootstrapped dataset
+* Say in every bootstrapped dataset we had 250 records not selected (random ones) we will get 250 * Total bootstrapped trees made i.e. 100 = 25,000 records to be checked for accuracy for Random Forest