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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

Out of Bag (OOB) Score for Bagging in Data Science

Parth Shukla
Parth Shukla Last Updated : 24 Jul, 2024
5 min read

Introduction

Bagging is one of the best-performing ensemble techniques used in data science, where multiple models of the same algorithms are taken as bootstrapping. The aggregation stage is performed as numerous outputs from various models are received, serving as the final output by calculating the mean of it in regression problems or returning the most frequent category in classification problems.

Out of Bag score or Out of bag error is the technique, or we can say it is a validation technique mainly used in the bagging algorithms to measure the error or the performance of the models in every epoch for reducing the total error of the models in the end.

This article will discuss the out-of-bag error, its significance, and its use case with its core intuition in bagging algorithms with examples of each. Here we will be studying and discussing the out-of-bag score for bagging in three parts: What, Why, and How?

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

Out of Bag Score: What is it?

Out of Bag score is the technique used in the bagging algorithms to measure each bottom model’s error for reducing the model’s absolute error, as we know that bagging is a process of summation of bootstrapping and aggregation. In the bootstrapping part, the data samples are taken and fed to the bottom models, and each bottom model makes trains on that. Finally, in the aggregation step, the prediction is done by bottom models and aggregated to get the final output from the model.

Out of Bag

In each step of the bootstrapping, a small part of the data points from the samples fed to the bottom learner is taken, and each bottom model makes predictions after being trained on the sample data. The prediction error on that sample is known as the out-of-bag error. The OOB score is the number of correctly predicted data on OOB samples taken for validation. It means that the more the error bottom model does, the Less the OOB score for the bottom model. Now, this OOB score is used as the error of the particular bottom models and depending upon this, the model’s performance is enhanced.

Out of Bag Score: Why Use it?

Now a question may arise, why the OOB Score is required? What is the need for that?

The OOB is calculated as the number of correctly predicted values by the bottom models on the validation dataset taken from the bootstrapped sample data. This OOB score helps the bagging algorithm understand the bottom models’ errors on anonymous data, depending upon which bottom models can be hyper-tuned.

For example, a decision tree of full depth can lead to overfitting, so let’s suppose we have a bottom model of the decision tree of the full depth and being overfitted on the dataset. Now in the case of overfitting, the error rate on the training data will be meager, but on the testing data, it will be very high. So the validation data will be taken from the bootstrapped sample, and the OOB score will be shallow. As the model is overfitting, the errors will be high on validation data which is entirely unknown and lead to the low OOB Score.

As we can see in the above example, the OOB score helps the model to understand the scenarios where the model is not behaving well and using which the final errors of the models can be reduced.

Out of Bag Score: How Does it Work?

Let’s try to understand how the OOB score works, as we know that the OOB score is a measure of the correctly predicted values on the validation dataset. The validation data is the sub-sample of the bootstrapped sample data fed to the bottom models. So here, the validation data will be recorded for every bottom model, and every bottom model will be trained on the bootstrapped samples. Once all the bottom models are trained on the fed selection, the validation samples will be used to calculate the OOB error of the bottom models.

Out of Bag
Source: https://miro.medium.com/max/850/1*JX-7eSfyvxEiAoHnulaiLQ.png

As we can see in the above image, the dataset sample contains a total of 1200 rows, out of which the three bootstrapped samples will be fed to the bottom model for training. Now from the bootstrap samples, 1,2, and 3, the small part or validation part of the data will be taken as OOB samples. These bottom models will be trained on the other part of the bootstrap samples, and once trained, the OOB samples will be used to predict the bottom models. Once the bottom models predict the OOB samples, it will calculate the OOB score. The exact process will now be followed for all the bottom models; hence, depending upon the OOB error, the model will enhance its performance.

To get the OOB Score from the Random Forest Algorithm, Use the code below.

from sklearn.trees import RandomForestClassifier
rfc = RandomForestClassifier(oob_score=True)
rfc.fit(X_train,y_train)
print(rfc.oob_score_)

The Advantages of the OOB Score

1. Better Performance of the model

As the OOB score indicates the error of the bottom models based on the validation data set, the model can get an idea about the mistakes and enhance the model’s performance.

2. No Data Leakage

Since the validation data for OOB samples are taken from the bootstrapped samples, the data is being used only for prediction, which means that the data will not be used for the training, which ensures that the data will not leak. The model will not see the validation data, which is quite good as the OOB score would be genuine if the data is kept secret.

3. Better For small datasets

OOB score is an excellent approach if the dataset size is small to medium. It performs so well on a small dataset and returns a better predictive model.

The Disadvantage of the OOB Score

1. High Time Complexity

As validation samples are taken and used for validating the model, it takes a lot of time to do the same process for multiple epochs; hence, the time complexity of the OOB score is very high.

2. Space Complexity

As some of the validation data is collected from bootstrap samples, now there will be more splits of the data in the model, which will result in more need of space to save and use the model.

2. Poor performance on Large Dataset

OOB score needs to perform better on large datasets due to space and time complexities.

Conclusion

In this article, we discussed the core intuition of the OOB score with three significant parts: what, why, and how. The advantages and disadvantages of the OOB score are also discussed, with the reasons behind them. Knowledge of these core concepts of the OOB score will help one understand the score better and use it for their models.

Some Key Takeaways from this article are:

1. OOB error is the measurement of the error of the bottom models on the validation data taken from the bootstrapped sample

2. OOB score helps the model understand the bottom model’s error and returns better predictive models.

3. OOB score performs so well on small datasets but [or large ones.

4. OOB score has high time complexity but ensures no data leakage.

Deepen your understanding of the OOB score and enhance your machine learning models! Join our ‘Mastering the OOB Score in Random Forest‘ course to explore its core concepts, advantages, and practical applications—empower your data science skills today!

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

Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

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