Skip to main content

Where SVM usage can be disadvantage for classification problem

 There are lot of limitations are associated with the usage of SVM for classification 

Please find below some areas: 

  1. SVM may not be suitable for large datasets:

    • SVMs can be less practical for very large datasets in terms of training time and memory usage. The computational complexity of SVMs increases with the number of data points, making them less efficient for big data scenarios. However, various techniques and optimizations have been developed to address this limitation, such as using stochastic gradient descent variants (e.g., SGDClassifier with linear kernel) or employing distributed computing frameworks.


  3. SVM performance with noisy data and overlapping classes:

    • SVMs are most effective when there is a clear margin of separation between classes. In cases where classes overlap significantly or the data contains noise, SVMs may struggle to find an optimal decision boundary. In such scenarios, other classification algorithms that are less sensitive to noise and class overlap, such as decision trees or random forests, may be more appropriate.


  5. Performance when features > samples:

    • SVMs can underperform when the number of features (dimensions) exceeds the number of training data samples. This situation can lead to overfitting because the SVM may try to fit the training data too closely, resulting in poor generalization to unseen data. Feature selection or dimensionality reduction techniques may be needed to address this issue.


  7. Lack of probabilistic explanation:

    • SVMs are primarily designed for binary classification and aim to find the hyperplane that maximizes the margin between classes. While they can be extended to multi-class classification, SVMs do not naturally provide probabilistic explanations for classification decisions, which can be a limitation in scenarios where probabilistic estimates are important. Other models like logistic regression provide direct probability estimates.

It's essential to recognize that there is no one-size-fits-all machine learning algorithm, and the choice of model depends on the characteristics of the data and the goals of the task. SVMs are powerful tools with strengths in specific situations, but they are not always the best choice. Data preprocessing, feature engineering, and model selection should be guided by a thorough understanding of the data and the problem domain. In practice, it's common to experiment with multiple algorithms to determine which one performs best for a particular task.

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