Random forest from scratch

🥦 RANDOM FOREST TEST 🥦

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Introduction

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Random forest is a popular machine learning algorithm that belongs to the supervised learning technique.

It can be used for both classification and regression and is based on the concept of ensemble learning i.e. a process of

combining multiple classifiers to solve a complex problem and to improve the performance of the model.

Random forest makes use of the bagging technique.

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How Random Forest test algorithm do that job 🌿🌿 ?

In random forest, the models M1, M2...etc. are nothing but decision trees. There are two phases namely 🌴: 🍂. Creating the random forest by combining N decision trees 🍂. Making predictions for each tree

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what is the main steps that this forest test will follow :

🍃. Starts by selecting random samples from given dataset using the bootstrap technique.

🍃. This algorithm will construct a decision tree for every sample, then it will get prediction result from each decision tree.

🍃. Next, voting will be performed for every predicted result.

🍃. At last, select the most voted prediction as the final prediction result.

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ponits to be noted

• 🌸 For a Random Forest Classifier :

the final result will be the majority vote.

• 🌸 For a Random Forest Regressor :

the final result will be mean of results of all the decision trees.

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