January 31, 2023

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

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One of the best performing techniques used in data science is where multiple models of the same algorithm are taken as bootstrapping. The aggregation step is performed as multiple outputs are obtained from different models, computing it as the final output 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 which is mainly used to measure the error in the bagging algorithm or to reduce the total error of the model after each Epoch is used to measure the performance of the model. End.

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This article will discuss the out-of-bag error in bagging algorithms, its importance, and its use case along with its basic intuition, with examples of each. Here we will study and discuss winning out-of-bag scores in three parts: What, Why and How?

Out of Bag Score: What is it?

Out of bag score is a technique used in bagging algorithm which measures the error of each lower model to reduce the absolute error of the model, as we know bagging is a process of summation of bootstrapping and aggregation. In the bootstrapping part, data samples are taken and fed to the bottom model, and each bottom model trains on it. Finally, in the aggregation phase, predictions are made by the model below and aggregated to obtain the final output from the model.

At each step of bootstrapping, a small portion of the data points are taken from the samples fed to the bottom learner, 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 the OOB samples taken for validation. This means that the more error the bottom model makes, the lower the OOB score for the bottom model. Now, this OOB score is used as the error of the particular bottom model and based on this, the performance of the model is enhanced.

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Out-of-Bag Score: Why Use It?

Now a question may arise that why OOB score is needed? What is the need of this?

OOB is calculated as the number of values ​​correctly predicted by the model below on the validation dataset taken from the bootstrapped sample data. This helps the OOB score bagging algorithm to understand the errors of the bottom model on anonymized data, depending on which bottom model can be hyper-tuned.

For example, full depth decision trees can lead to overfitting, so suppose we have the bottom model of a full depth decision tree and are overfitting on the dataset. Now in case of overfitting, there will be error rate on training data, but it will be very high on test data. So the validation data will be taken from the bootstrapped sample, and the OOB score will be shallow. As the model is overfitting, errors will be high on validation data which are completely unknown and lead to low OOB scores.

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 model can be minimized.

Out of Bag Score: How does it work?

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

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

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

To get OOB score from Random Forest algorithm, use the below code.

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

1. Better performance of the model

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

2. No Data Leakage

Since the validation data for the OOB samples is taken from the bootstrapped samples, the data is only being used for prediction, which means the data will not be used for training, which ensures that no data leaks Will happen. Model validation will not look at the data, which is good enough because the OOB score will be real if the data is kept secret.

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3. Better Dataset

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

Loss of OOB Score

1. High Time Complexity

As validation samples are taken and used to validate the model, doing the same process for multiple epochs is very time consuming; Therefore, the time complexity of the OOB score is very high.

2. Space Complexity

Since some of the validation data is collected from the bootstrap samples, there will now be more splits of data in the model, resulting in more space being required to store and use the model.

2. Poor performance on large datasets

The OOB score needs to perform optimally on large datasets due to the complexities of space and time.

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

Some important results from this article are:

1. OOB error is a measure of the error of the model below on the validation data taken from the bootstrapped sample

2. The OOB score helps the model understand the error of the bottom model and return a better predictive model.

3. The OOB score performs so well on small datasets[orlargeones[orlargeones[याबड़ेवाले।[orlargeones

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

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