UNSUPERVISED LEARNING
- Unsupervised learning helps in identifying hidden patterns and structures in data without any prior assumptions. This is crucial for understanding the inherent distribution and relationships within the data.
- Helps in identifying the most important features, thus reducing the dimensionality of the data and potentially improving the performance of supervised learning models.
- Unsupervised learning can identify user preferences and behaviour patterns to provide personalized recommendations without needing explicit feedback.
- Identifying unusual patterns or outliers in transaction data can help detect fraudulent activities.
After applying the appropriate algorithm, the algorithm groups the data objects based on the similarities and differences among them.
- Clustering: Clustering is a fundamental technique in unsupervised
machine learning that involves partitioning a dataset into distinct groups, or
clusters, such that items in the same cluster are more similar to each other
than to those in other clusters. Clustering helps in discovering inherent
structures in the data, making it useful for a variety of applications, from
market segmentation to anomaly detection.
- Sub-Types:
- Anomaly detection: Unsupervised learning can identify unusual patterns or deviations from normal behaviour in data, enabling the detection of fraud, intrusion, or system failures.
- Scientific discovery: Unsupervised learning can uncover hidden relationships and patterns in scientific data, leading to new hypotheses and insights in various scientific fields.
- Recommendation systems: Unsupervised learning can identify patterns and similarities in user behaviour and preferences to recommend products, movies, or music that align with their interests.
- Image analysis: Unsupervised learning can group images based on their content, facilitating tasks such as image classification, object detection, and image retrieval.
- It does not require training data to be labelled.
- Capable of finding previously unknown patterns in data.
- Unsupervised learning can help you gain insights from unlabelled data that you might not have been able to get otherwise.
- Unsupervised learning is good at finding patterns and relationships in data without being told what to look for. This can help you learn new things about your data.
- Difficult to measure accuracy or effectiveness due to lack of predefined answers during training.
- The results often have lesser accuracy.
- The user needs to spend time interpreting and label the classes which follow that classification.
- Unsupervised learning can be sensitive to data quality, including missing values, outliers, and noisy data.
Parameters |
Supervised machine learning |
Unsupervised machine learning |
Input Data |
Algorithms are trained using labeled data. |
Algorithms are used against data that is not labeled |
Computational Complexity |
Simpler method |
Computationally complex |
Accuracy |
Highly accurate |
Less accurate |
No. of classes |
No. of classes is known |
No. of classes is not known |
Data Analysis |
Uses offline analysis |
Uses real-time analysis of data |
Algorithms used |
Linear and Logistics regression, Random forest,
multi-class classification, decision tree, Support Vector Machine, Neural
Network, etc. |
K-Means clustering, Hierarchical
clustering, KNN,etc. |
Output |
Desired output is given. |
Desired output is not given. |
Training data |
Use training data to infer model. |
No training data is used. |
Example |
Example: Optical character recognition. |
Example: Find a face in an image. |
Very informative and well-written!
ReplyDeleteNice work!
ReplyDelete