Passive and Active learning in Machine Learning

Machine learning is a subfield of artificial intelligence that deals with the creation of algorithms that can learn and improve themselves without explicit programming. One of the most critical factors that contribute to the success of a machine learning model is the quality and quantity of data used to train it. Passive learning and active learning are two approaches used in machine learning to acquire data.

Passive Learning:

Passive learning, also known as batch learning, is a method of acquiring data by processing a large set of pre-labeled data. In passive learning, the algorithm uses all the available data to learn and improve its performance. The algorithm does not interact with the user or request additional data to improve its accuracy.

Example:- An example of passive learning is training a machine learning model to classify emails as spam or not spam. The algorithm is fed a large dataset of labeled emails and uses it to learn how to identify spam emails. Once the training is complete, the algorithm can accurately classify new emails without any further input from the user.

Active Learning:

Active learning is a method of acquiring data where the algorithm interacts with the user to acquire additional data to improve its accuracy. In active learning, the algorithm starts with a small set of labeled data and requests the user to label additional data. The algorithm uses the newly labeled data to improve its performance and may continue to request additional data until a satisfactory level of accuracy is achieved.

Example:- An example of active learning is training a machine learning model to recognize handwritten digits. The algorithm may start with a small set of labeled data and ask the user to label additional data that the algorithm is uncertain about. The algorithm uses the newly labeled data to improve its accuracy, and the process repeats until the algorithm can accurately recognize most handwritten digits.

Passive learning and Active learning

Difference Between Passive Learning and Active Learning:

The following table summarizes the differences between passive learning and active learning:

Passive Learning	Active Learning
Uses a large set of pre-labeled data to train the algorithm	Starts with a small set of labeled data and requests additional data from the user
The algorithm does not interact with the user	The algorithm interacts with the user to acquire additional data
It does not require user input after training is complete	May continue to request additional data until a satisfactory level of accuracy is achieved
Suitable for applications where a large dataset is available	Suitable for applications where labeled data is scarce or expensive to acquire

Conclusion:

In conclusion, passive learning and active learning are two approaches used in machine learning to acquire data. Passive learning uses a large set of pre-labeled data to train the algorithm, while active learning starts with a small set of labeled data and requests additional data from the user to improve accuracy. The choice between passive learning and active learning depends on the availability of labeled data and the application’s requirements.