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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

A Map to Avoid Getting Lost in “Random Forest”

Shivam
Shivam Last Updated : 02 May, 2021
4 min read

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

The Random Forest algorithm is undoubtedly one of the most popular algorithms among data scientists. It performs very well in both classification and regression problems. Random Forest is known as an ensemble technique for it is a collection of multiple decision trees.

What was the main objective of using the multiple decision trees? 

Using a single decision tree has several drawbacks. When we use a single decision tree to solve a problem statement, we encounter a situation of low bias and high variance. That is, the tree will capture all of the information about the training data as well as the noise. As a result, the model developed using the decision tree algorithm will perform nicely with training data but will perform poorly when evaluated on testing data (unknown data). Overfitting is the condition of having low bias and high variance. 

Remember: –

Decision Tree—————> Overfitting————–> low bias high variance 

Random Forest uses multiple decision trees to avoid this issue present in the decision tree algorithm.  

But, how does Random Forest address the problem of overfitting?

The Random Forest algorithm does not use all of the training data when training the model, as seen in the diagram below. Instead, it performs rows and column sampling with repetition. This means that each tree can only be trained with a limited number of rows and columns with data repetition. In the following diagram, training data 1 is used to train decision tree 1, and training data n is used to train decision tree n. However, since each tree is created to its full depth and has the property of overfitting, how do we avoid this problem?

Since the algorithm isn’t reliant on a particular decision tree’s outcome. It will first get the results from all of the decision trees, and then give the final result based on the problem statement type. For instance; if the problem statement type is classification, it would use majority voting. suppose we are classifying “yes” and “no” with 10 trees, if 6 trees are classifying “yes” and 4 are classifying “no”, the final answer will be “yes” using majority voting. What if our output is a continuous variable? In that case, the final result would be either the mean or the median of all trees’ output.

Classification Problem -> Majority Voting

Regression Problem -> Mean / Median

 

 

Random forest structure
https://images.app.goo.gl/pwKrDydww8ReJbXf8

Here,

The model performs row sampling. However, feature sampling must be done according to the problem statement type.

  • If the problem statement type is “classification”.

The total number of random features/ columns selected = p^½ or the square root of p,

 where p is the total number of independent attributes/ features present in the data.

  • If the problem statement type is “regression”.

The total number of random columns selected = p/3.

Random Forest avoids overfitting with:-

1) Performing row and feature sampling.

2) Connecting all the decision trees parallelly.

Why it is known as the Bootstrap Aggregation technique?

Random Forest is a type of ensemble technique, also known as bootstrap aggregation or bagging.

The process of sampling different rows and features from training data with repetition to construct each decision tree model is known as bootstrapping, as shown in the following diagram.

Aggregation is the process of taking all of the outcomes from each decision tree and combining them to produce a final outcome using either majority votes or average values, depending on the problem statement type.

Aggregation modelling Random Forest
https://images.app.goo.gl/mpTaAhPvx964iPnw7

Random Forest using R

library(caTools)

library(randomForest)

We require to install ‘caTools’ and ‘randomForest’ libraries and activate them using the library() function

We have used the data set of banknote authentication and stored it in the ‘data’ variable. we will check the structure of data using the str() function.

data <- read.csv(‘bank_note_data.csv’, header=T)
str(data) 

random forest data describe

 

Now we will split our data into train and test parts. 80% for training and 20 % for testing the model.

set.seed(123)
split <- sample.split(data,SplitRatio = 0.8)
train <- subset(data,split==T)
test <- subset(data,split==F)

After splitting the data, we will build our model using the randomForest() function. Here ‘ntree’ is the hyperparameter. which is required to be tuned. In this case, it is selected as 500.

 

random_model <- randomForest(Class~., data=train, mtry=2, ntree=500)

 

Predicting the model’s accuracy on the test data using the predict() function.

eval <- predict(random_model,test)

Evaluate the model’s accuracy using the confusion matrix

confusionMatrix(table(eval,test$Class))

 

confusion matrix

 

The model is giving an accuracy of 98.91% on the test data. This ensures that Random Forest is doing a fantastic job.

Thank You,

Shivam Sharma.

Phone: +91 7974280920

E-Mail: [email protected]

LinkedIn: www.linkedin.com/in/shivam-sharma-49ba71183

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

Shivam
Shivam

Meet Shivam, a data scientist with two years of experience currently working at Mercedes Benz. I have worked on multiple projects related to natural language processing, classical machine learning, and deep learning. With my learnings in data science, I am also skilled at analyzing complex data sets to uncover insights and trends that drive business decisions.

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