Skip to main content

What is the difference between Bagging and Booting?

Bagging and boosting are both ensemble techniques used in machine learning to improve the performance of models. Here's a comparison of the two techniques along with some use cases:

Bagging (Bootstrap Aggregating):

  • Overview: Bagging is an ensemble technique where multiple models are trained independently on different subsets of the training data and their predictions are averaged or voted upon to make a final prediction.


  • Usage: Bagging is often used when you have a high-variance model that tends to overfit the data. It helps reduce the variance of the model by averaging predictions from multiple models.


  • How to Use:

    1. Split the training data into multiple subsets (bootstrap samples) with replacement.
    2. Train a base model on each subset.
    3. Combine the predictions from all base models by averaging (for regression) or voting (for classification).


  • Use Cases:

    • Random Forest: Bagging is used to train multiple decision trees independently, and the final prediction is determined by voting or averaging the outputs. It's widely used for classification and regression tasks.
    • Bagged Decision Trees: Bagging can be applied to other base models like SVM, k-Nearest Neighbors, and neural networks.

Boosting:

  • Overview: Boosting is an ensemble technique where multiple weak learners (models that perform slightly better than random guessing) are combined into a strong learner. Each new learner is trained to correct the errors made by the previous ones.


  • Usage: Boosting is used when you want to improve the performance of a weak model and reduce both bias and variance. It focuses on correcting misclassifications made by earlier models.


  • How to Use:

    1. Train a base model (usually a weak learner) on the entire training dataset.
    2. Give more weight to misclassified samples in the next iteration.
    3. Train a new model that focuses on the previously misclassified samples.
    4. Repeat steps 2 and 3 for a predefined number of iterations or until a stopping criterion is met.
    5. Combine the predictions of all models using weighted voting.

  • Use Cases:


    • AdaBoost (Adaptive Boosting): AdaBoost is used primarily for binary classification problems. It combines the outputs of multiple weak classifiers (e.g., decision trees, SVMs) to create a strong classifier.
    • Gradient Boosting: Algorithms like Gradient Boosting, XGBoost, and LightGBM are used for regression and classification tasks. They sequentially build decision trees to correct errors made by previous trees.
    • Face Detection: Boosting techniques are used in computer vision tasks like face detection, where weak classifiers are combined to detect faces accurately.

In summary, bagging is used to reduce the variance of a model by averaging predictions from independently trained models, while boosting focuses on reducing both bias and variance by sequentially training models that correct each other's errors. The choice between these techniques depends on your specific problem and the characteristics of your data.

Labels:

Comments

Post a Comment

Popular posts from this blog

Error: could not find function "read.xlsx" while reading .xlsx file in R

Got this during the execution of following command in R > dat Error: could not find function "read.xlsx" Tried following command > install.packages("xlsx", dependencies = TRUE) Installing package into ‘C:/Users/amajumde/Documents/R/win-library/3.2’ (as ‘lib’ is unspecified) also installing the dependencies ‘rJava’, ‘xlsxjars’ trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/rJava_0.9-8.zip' Content type 'application/zip' length 766972 bytes (748 KB) downloaded 748 KB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsxjars_0.6.1.zip' Content type 'application/zip' length 9485170 bytes (9.0 MB) downloaded 9.0 MB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsx_0.5.7.zip' Content type 'application/zip' length 400968 bytes (391 KB) downloaded 391 KB package ‘rJava’ successfully unpacked and MD5 sums checked package ‘xlsxjars’ successfully unpacked ...
Post a Comment
Read more

Training LLM model requires more GPU RAM than storing same LLM

Storing an LLM model and training the same model both require memory, but the memory requirements for training are typically higher than just storing the model. Let's dive into the details: Memory Requirement for Storing the Model: When you store an LLM model, you need to save the weights of the model parameters. Each parameter is typically represented by a 32-bit float (4 bytes). The memory requirement for storing the model weights is calculated by multiplying the number of parameters by 4 bytes. For example, if you have a model with 1 billion parameters, the memory requirement for storing the model weights alone would be 4 GB (4 bytes * 1 billion parameters). Memory Requirement for Training: During the training process, additional components use GPU memory in addition to the model weights. These components include optimizer states, gradients, activations, and temporary variables needed by the training process. These components can require additional memory beyond just storing th...
Post a Comment
Read more

What is the benefit of using Quantization in LLM

Quantization is a technique used in LLMs (Large Language Models) to reduce the memory requirements for storing and training the model parameters. It involves reducing the precision of the model weights from 32-bit floating-point numbers (FP32) to lower precision formats, such as 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Bottomline: You can use Quantization to reduce the memory footprint off the model during the training. The usage of quantization in LLMs offers several benefits: Memory Reduction: By reducing the precision of the model weights, quantization significantly reduces the memory footprint required to store the parameters. This is particularly important for LLMs, which can have billions or even trillions of parameters. Quantization allows these models to fit within the memory constraints of GPUs or other hardware accelerators. Training Efficiency: Quantization can also improve the training efficiency of LLMs. Lower precision formats require fewer computati...
Post a Comment
Read more