Sampling Techniques for Supervised or Unsupervised Tasks (Unsupervised and

Sampling Techniques for Supervised or Unsupervised Tasks (Unsupervised and

Price : 139.97

Ends on : N/A

View on eBay
Supervised Learning)

When working on supervised or unsupervised tasks in machine learning, sampling techniques play a crucial role in determining the quality and accuracy of the model’s predictions. In this post, we will discuss some common sampling techniques used in both supervised and unsupervised learning tasks.

Supervised Learning:

1. Random Sampling: This is the simplest form of sampling where data points are randomly selected from the dataset without any specific criteria. This method is commonly used when the dataset is large and representative of the population.

2. Stratified Sampling: In this technique, the dataset is divided into different strata based on certain characteristics (e.g., class labels) and then samples are randomly selected from each stratum. This ensures that each class is represented in the sample proportionally to its representation in the dataset.

3. Oversampling and Undersampling: In cases where the dataset is imbalanced, oversampling (duplicating minority class samples) and undersampling (removing majority class samples) techniques can be used to balance the dataset and improve the model’s performance.

Unsupervised Learning:

1. K-Means Sampling: In this technique, data points are clustered into k clusters based on their similarity and then samples are selected from each cluster. This helps in reducing the computational complexity and improving the efficiency of the model.

2. Hierarchical Sampling: In hierarchical sampling, data points are grouped into a hierarchy of clusters based on their similarity. Samples are then selected from different levels of the hierarchy to ensure diversity in the sample.

3. Density-Based Sampling: This technique selects samples based on the density of data points in the dataset. Data points in high-density regions are more likely to be selected as samples, while data points in low-density regions are less likely to be selected.

In conclusion, sampling techniques play a crucial role in both supervised and unsupervised learning tasks. By choosing the right sampling strategy, researchers and data scientists can improve the quality and accuracy of their models and make more informed decisions based on the data.
#Sampling #Techniques #Supervised #Unsupervised #Tasks #Unsupervised