Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Lets Open the Black Box of Random Forests

Ujwal
Ujwal Last Updated : 04 Dec, 2020
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Random Forests are always referred to as black-box models. Let’s try to crack open it and see what is inside it. Are you ready?

BRACE FOR IMPACT! BRACE! BRACE! BRACE!

Oops!!! Our plane has crashed, but fortunately, we all are safe. We are Data scientists, so we want to open the black box and see what random things have been recorded inside it.  Yes, let’s come to our topic.

 

What are Random forests? 

You must have at least once solved a problem of probability in your high-school in which you were supposed to find the probability of getting a specific colored ball from a bag containing different colored balls, given the number of balls of each color. Random forests are simple if we try to learn them with this analogy in mind.

Random forests (RF) are basically a bag containing n Decision Trees (DT) having a different set of hyper-parameters and trained on different subsets of data. Let’s say I have 100 decision trees in my Random forest bag!! As I just said, these decision trees have a different set of hyper-parameters and a different subset of training data, so the decision or the prediction given by these trees can vary a lot. Let’s consider that I have somehow trained all these 100 trees with their respective subset of data. Now I will ask all the hundred trees in my bag that what is their prediction on my test data. Now we need to take only one decision on one example or one test data, we do it by taking a simple vote. We go with what the majority of the trees have predicted for that example.

Random Forests Diagram

In the above picture, we can see how an example is classified using n trees where the final prediction is done by taking a vote from all n trees.

In machine learning language, RFs are also called an ensemble or bagging method. I think the bagging word might have come from the analogy that we have just discussed !!!

 

Let’s come a bit closer to ML Jargons!!

 The Random forest is basically a supervised learning algorithm. This can be used for regression and classification tasks both. But we will discuss its use for classification because it’s more intuitive and easy to understand. Random forest is one of the most used algorithms because of its simplicity and stability.

While building subsets of data for trees, the word “random” comes into the picture. A subset of data is made by randomly selecting x number of features (columns) and y number of examples (rows) from the original dataset of n features and m examples.

Random forests are more stable and reliable than just a decision tree. This is just saying like- it’s better to take a vote from all cabinet ministers rather than just accepting the decision given by the PM.

As we have seen that the Random Forests are nothing but the collection of decision trees, it becomes essential to know the decision tree. So let’s dive deep into decision trees.

 

What is a Decision Tree?

In very simple words, it is a “set of rules” created by learning on a dataset that can be used to do predictions on future data. We will try to understand this with one example.

Decision Tree

A simple small dataset is shown here. In this dataset, the first four features are independent features and the last one is the dependent features. The independent features describe the weather condition on a given day and the dependent feature tells us whether we were able to play tennis on that day or not.

Now we will try to create some rules using independent features to predict dependent features. Just by observation, we can see that if Outlook is Overcast then the Play is always yes irrespective of other features. Similarly we can create all rules to fully describe the data set. Here are all the rules.

    • R1: If (Outlook=Sunny) AND (Humidity=High) Then Play=No
    • R2: If (Outlook=Sunny) AND (Humidity=Normal) Then Play=Yes
    • R3: If (Outlook=Overcast) Then Play=Yes
    • R4: If (Outlook=Rain) AND (Wind=Strong) Then Play=No
    • R5: If (Outlook=Rain) AND (Wind=Weak) Then Pay=Yes

We can easily convert these rules into a tree chart. Here is the tree chart.

Random forests - Decision Tree Diagram

By observing Data, Rules, and Tree, you will understand that we can now predict whether we should play tennis or not, given the situation of the weather based on independent features. This whole process of creating rules for a given data is nothing but the training of the decision tree model.

We could make rules and make a tree by just observation here because the dataset is very small. But how do we train decision tree on a bigger dataset? For that, we need to know a little bit of mathematics. Now will try to understand the mathematics behind the decision tree.

 

Mathematical concepts behind Decision Tree

This section consists of two important concepts- Entropy and Information gain.

Entropy 

Entropy is a measure of the randomness of a system. The entropy of sample space S is the expected number of bits needed to encode the class of a randomly drawn member of S.  Here we have 14 rows in our data so 14 members.

Entropy E(S)= -∑p(x)*log2(p(x))

The entropy of the system is calculated by the above formula where p(x) is the probability of getting class x from those 14 members. We have two classes here one is Yes and the Other is No in the Play column. We have 9 Yes and 5 No in our dataset. So entropy calculation here will be as below

E(S)= -[p(Yes)*log(p(Yes))+ p(No)*log(p(No))]=-[(9/14)*log((9/14))+ (5/14*log((5/14)))]=0.94

 

Information Gain

The information gain is the amount by which the Entropy of the system reduces due to the split that we have done. We have created the tree using observations. But how come we decided that we should first split the data based on Outlook and not on any other feature? The reason is that this split was reducing the entropy by the maximum amount but we did it intuitively in the above example.

Random Forests - Gain Formula
Gain Diagram

The tree split above shows us that 9 YES and 5 No have been split as (2 Y, 3 N), (4 Y, 0 N), (3 Y, 2 N), when we do the split based on outlook. The E values below every split show entropy values considering themselves as a whole system and using the entropy formula above. Then we have calculated the Information gain for the outlook split by using the above gain formula.

Similarly, we can calculate information gain for each feature split independently. And we get the below results:

    • Gain(S, Outlook) =0.247
    • Gain(S, Humidity) =0.151
    • Gain(S, Wind) =0.048
    • Gain(S, Temperature) =0.029

We can see that we are getting maximum information gain by splitting on the outlook feature. We repeat this procedure to create the whole tree. I hope you have enjoyed reading the article. If you like the article please share it with your colleagues and friends. Happy Reading!!!

Ujwal
Ujwal

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details