There are different types of Machine Learning with Python; two of the most fundamental approaches are supervised learning and unsupervised learning. The difference between these two sets the tone for appropriately applying the type to the real problem. In this blog, we will look at both methods and try to give simple examples using Python to explain the application.
Supervised Learning
Supervised learning is machine learning by which a model is trained from labelled data, meaning that this dataset has input data, general features and output, generally the target. The model learns the combination between the input and output to make predictions on new, unseen data.
Key Characteristics of Supervised Learning
- It needs labelled data (input/output).
- Used for classification, like spam/not spam e-mails and regret, sessions such as the prediction of house prices.
- Examples: Linear regression, decision tree, support vector machine.
Unsupervised Learning
On the other hand, unsupervised learning pertains to unlabeled data. The model is not provided with the right outputs, or for that matter, the target, instead it should figure out the structure as well as patterns in the data. It is more a typical task used for clustering or grouping similar data points and to reduce dimensionality-to simplify data while still getting important information.
Key Characteristics of Unsupervised Learning:
- It does not require labelled data.
- Used for detecting unseen patterns, grouping, and data exploration.
- Examples: K-means clustering, principal component analysis (PCA) and hierarchical clustering.
Differences Between Supervised and Unsupervised
Input Data:
- Supervised: Labelled (features + target/output)
- Unsupervised: Unlabelled (only features)
Goal:
- Supervised: Predict a target from input data
- Unsupervised: Discover patterns/structures in data
Common Algorithms:
- Supervised: Linear regression, decision trees, neural nets
- Unsupervised: K-means clustering, PCA, hierarchical clustering
Applications:
- Supervised: Spam detection, image classification, price prediction
- Unsupervised: Market segmentation, recommendation systems, anomaly detection
Data Requirement:
- Supervised: Large labelled datasets
- Unsupervised: Can work with unlabelled data
Key Difference:
- Supervised: Learns with labelled data
- Unsupervised: Finds patterns in unlabelled data
The approaches in Supervised and Unsupervised Learning
While deciding on the appropriate approach, you can follow a few steps.
Availability of labelled data: If there is labelled data, then you must go through a supervised learning process. If unlabelled no such thing as labelled data exists, then unsupervised learning can be analysed and hence help discover the patterns hidden in the data.
Goal Prediction: To be able to predict an outcome, such as whether an email is spam, supervised learning is appropriate. However, when the objective is pattern finding or segmenting data, for example, grouping customers by their buying behaviour, learning performs the best.
Problem complexity: Supervised learning might be easier because the output is overtly found; unsupervised learning would probably require more exploratory analysis due to no guide labels present.
Conclusion
The functionality of distinguishing between supervised and unsupervised learning stands as a crucial characteristic for properly applying Machine Learning with Python. Supervised learning fares well when there is available data with labels; thus, it is very well utilised in classification and regression tasks. Unsupervised learning emerges as an excellent alternative in the identification of hidden patterns and insights within unlabelled data. It is helpful to understand both models. The London School of Emerging Technology (LSET) provides you a chance to enrol in Machine Learning with Python course, where you can learn in-depth about ML concepts of Python and put your hands on practical knowledge through their internship opportunities that can make you job-ready.