Key Difference – Supervised vs Unsupervised Machine Learning
Supervised learning and unsupervised learning are two core concepts of machine learning. Supervised Learning is a Machine Learning task of learning a function that maps an input to an output based on the example input-output pairs. Unsupervised Learning is the Machine Learning task of inferring a function to describe hidden structure from unlabelled data. The key difference between supervised and unsupervised machine learning is that supervised learning uses labeled data while unsupervised learning uses unlabeled data.
Machine Learning is a field in Computer Science that gives the ability for a computer system to learn from data without being explicitly programmed. It allows to analyse the data and to predict patterns in it. There are many applications of machine learning. Some of them are face recognition, gesture recognition and speech recognition. There are various algorithms related to machine learning. Some of them are regression, classification and clustering. The most common programming languages for developing machine learning based applications are R and Python. Other languages such as Java, C++ and Matlab can also be used.
CONTENTS
1. Overview and Key Difference
2. What is Supervised Learning
3. What is Unsupervised Learning
4. Similarities Between Supervised and Unsupervised Machine Learning
5. Side by Side Comparison – Supervised vs Unsupervised Machine Learning in Tabular Form
6. Summary
What is Supervised Learning?
In machine learning based systems, the model works according to an algorithm. In supervised learning, the model is supervised. First, it is required to train the model. With the gained knowledge, it can predict answers for the future instances. The model is trained using a labelled dataset. When an out of sample data is given to the system, it can predict the result. Following is a small extract from the popular IRIS dataset.
According to the above table, Sepal length, Sepal width, Patel length , Patel width and Species are called the attributes. The columns are known as features. One row has data for all attributes. Therefore, one row is called an observation. The data can either be numerical or categorical. The model is given the observations with the corresponding species name as the input. When a new observation is given, the model should predict the type of species which it belongs to.
In supervised learning, there are algorithms for classification and regression. Classification is the process of classifying the labeled data. The model created boundaries that separated the categories of data. When new data is provided to the model, it can categorize based on where the point exists. The K-Nearest Neighbors (KNN) is a classification model. Depending on the k value, the category is decided. For example, when k is 5, if a particular data point is near to eight data points in category A and six data points in category B, then the data point will be classified as A.
The regression is the process of predicting the trend of the previous data to predict the outcome of the new data. In regression, the output can consist of one or more continuous variables. Prediction is done using a line that covers most data points. The simplest regression model is a linear regression. It is fast and does not require tuning parameters such as in KNN. If the data shows a parabolic trend, then the linear regression model is not suitable.
Those are some examples of supervised learning algorithms. Generally, the results generated from supervised learning methods are more accurate and reliable because the input data is well known and labelled. Therefore, the machine has to analyze only the hidden patterns.
What is Unsupervised Learning?
In unsupervised learning, the model is not supervised. The model work on its own, to predict the outcomes. It uses machine learning algorithms to come to conclusions on unlabeled data. Generally, the unsupervised learning algorithms are harder than supervised learning algorithms because there is few information. Clustering is a type of unsupervised learning. It can be used to group the unknown data using algorithms. The k-mean and density-based clustering are two clustering algorithms.
k-mean algorithm, places k centroid randomly for each cluster. Then each data point is assigned to the closest centroid. Euclidean distance is used to calculate the distance from the data point to the centroid. The data points are classified into groups. The positions for k centroids are calculated again. The new centroid position is determined by the mean of all points in the group. Again each data point is assigned to the closest centroid. This process repeats until the centroids no longer change. k-mean is a fast clustering algorithm, but there is no specified initialization of clustering points. Also, there is a high variation of clustering models based on initialization of cluster points.
Another clustering algorithm is Density based clustering. It is also known as Density Based Spatial Clustering Applications with noise. It works by defining a cluster as the maximum set of density connected points. They are two parameters used for density based clustering. They are Ɛ (epsilon) and minimum points. The Ɛ is the maximum radius of the neighborhood. The minimum points are the minimum number of points in the Ɛ neighbourhood to define a cluster. Those are some examples of clustering that falls into unsupervised learning.
Generally, the results generated from unsupervised learning algorithms are not much accurate and reliable because the machine has to define and label the input data before determining the hidden patterns and functions.
What is the Similarity Between Supervised and Unsupervised Machine Learning?
- Both Supervised and Unsupervised Learning are types of Machine Learning.
What is the Difference Between Supervised and Unsupervised Machine Learning?
Supervised vs Unsupervised Machine Learning |
|
| Supervised Learning is the Machine Learning task of learning a function that maps an input to an output based on example input-output pairs. | Unsupervised Learning is the Machine Learning task of inferring a function to describe hidden structure from unlabeled data. |
| Main Functionality | |
| In supervised learning, the model predicts the outcome based on the labelled input data. | In unsupervised learning, the model predicts the outcome without labelled data by identifying the patterns on its own. |
| Accuracy of the Results | |
| The results generated from supervised learning methods are more accurate and reliable. | The results generated from unsupervised learning methods are not much accurate and reliable. |
| Main Algorithms | |
| There are algorithms for regression and classification in supervised learning. | There are algorithms for clustering in unsupervised learning. |
Summary – Supervised vs Unsupervised Machine Learning
Supervised Learning and Unsupervised Learning are two types of Machine Learning. Supervised Learning is the Machine Learning task of learning a function that maps an input to an output based on example input-output pairs. Unsupervised Learning is the Machine Learning task of inferring a function to describe hidden structure from unlabeled data. The difference between supervised and unsupervised machine learning is that supervised learning uses labelled data while unsupervised leaning uses unlabeled data.
Reference:
1.TheBigDataUniversity. Machine Learning – Supervised VS Unsupervised Learning, Cognitive Class, 13 Mar. 2017. Available here
2.“Unsupervised Learning.” Wikipedia, Wikimedia Foundation, 20 Mar. 2018. Available here
3.“Supervised Learning.” Wikipedia, Wikimedia Foundation, 15 Mar. 2018. Available here
Image Courtesy:
1.’2729781′ by GDJ (Public Domain) via pixabay