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What do you do when your text or dataset doesn’t have any labels? Types of Unsupervised Learning Algorithms is a group of ML algorithms & approaches that work with “no-ground-truth” data.
What is Unsupervised Learning?
Unsupervised ML Algorithms cannot be applied to a regression since it is unknown what the output values/results could be, making it impossible to train the algorithm how you normally would.
The best way to understand what’s going on here is to think of a basic test. When you took tests in school/college, there were questions & answers; your grade was calculated by how close your answers were to the actual ones (or the answer key). But just think if there was no answer key, & there were only questions. How would you grade yourself?
Now apply this framework/model to ML algorithms. Standard text or datasets in Machine Learning Algorithms have labels (think: the answer key) & follow the logic of “X leads to Y.” Let’s say we might want to figure out if people with more Twitter followers generally make higher money. We think that our input (which in this case is Twitter followers) might lead to our output (money), & we try to approximate what that relationship is.
The stars are data points, & ML algorithms works on creating a line that explains how the input & outcomes are related. But in the types of Unsupervised Learning Algorithms, there are no outcomes! We’re just looking to analyze the input, which is our Twitter followers. There is no ‘money,’ or Y, involved at all. Such as there not being an answer key for the school test.
Maybe we don’t have access to ‘money’ data or are just interested in several different questions. It doesn’t matter at all! The key thing is that there is no output to match to & no line to draw that shows a relationship.
So what exactly is the goal of unsupervised learning algorithms then? What actions do we perform when we only have input data without labels?
Why Use Unsupervised Learning?
Here are major reasons for using ‘Unsupervised Learning Algorithms’:
- Unsupervised Machine Learning Algorithms finds all kinds of unknown patterns in data through pattern recognition.
- It takes place in real-time, so all the input data to be analyzed & labeled in learners’ presence.
- Unsupervised algorithms/methods help you to search for features that can be useful for categorization.
Types of Unsupervised Learning Algorithms
Below are the two major types of Unsupervised Learning Algorithms.
Any company or business needs to focus on understanding customers: who they are & what’s driving their buying decisions?
You’ll usually have several groups of users that can be split across a few criteria. These criteria can be as simple: age & gender or as complex as a persona & purchase process. Types of Unsupervised learning Algorithms can help you accomplish this task automatically.
Clustering will run through your data & find these natural clusters if they exist. For your visitors, that might mean one cluster of 30-something artists & another of millennials who own pets. You can generally modify the number of clusters your ML algorithm looks for, which lets you adjust these groups’ granularity. There are many types of clustering you can utilize:
- K-Means Clustering: Clustering your data points or text into a character “K” of mutually exclusive clusters. A lot of the complexity surrounds how you should pick the appropriate number for K.
- Hierarchical Clustering: Clustering your data points into parent & child clusters. You might split your customers between younger & older ages & then split each of those groups into their clusters as well.
- Probabilistic Clustering: Clustering your data points or text into clusters on a probabilistic scale.
These variations on the same fundamental procedure might look something like this in code:
Any clustering ML algorithm will typically output all of your data points & the other number of clusters to which they belong. It’s totally up to you to decide what they mean & exactly what the ML algorithm has found. As with much of data science – Unsupervised Learning can only do so much: value is created when humans interface with outputs & create meaning.
Even with some major advances over the past few years in computing power & storage costs, it still makes sense to keep your data sets as small & reliable as possible. That clearly means only running ML algorithms on necessary data & not training on too much. Unsupervised Learning Algorithms can help with that through a procedure known as dimensionality reduction.
Dimensionality reduction (dimensions refers to how many columns are in your dataset) relies on many of the same concepts as Information Theory: it assumes that a lot of data is redundant & that you can represent most of the data in a data set with only a small fraction of the actual content.
In general, this means combining parts of your knowledge in unique ways to convey meaning. There are a couple of famous ML algorithms commonly used to decrease dimensionality:
- Principal Component Analysis (PCA): Finds the linear combinations that communicate most of your data set variance.
- Singular-Value Decomposition (SVD): Factorizes your details into the product of three other, smaller matrices.
These techniques and some of their more complex cousins all rely on linear algebra concepts to break down a matrix into more digestible & informatory pieces.
Reducing the dimensionality of your information can be a crucial part of a good ML pipeline. Take this example of an image-centerpiece for the burgeoning discipline of computer vision.
If you could decrease the size of your training set by order of magnitude, that will significantly lower your compute & storage costs while making your ML models run that much faster. That’s why PCA is often run on images during preprocessing in mature ML pipelines.
Generative models are a class of Unsupervised Learning Algorithms in which training data are given & new samples are generated from the same distribution. These ML models must discover & efficiently learn the essence of the given set of data to generate similar data. This type of model’s long-term benefit is its ability to learn the given data’s features automatically.
A basic example of generative models is an image dataset or text. Given a set of basic images, a generative model could generate a set of images similar to the given set.
Unsupervised Learning Algorithms:
Below is the list of some popular Unsupervised ML Algorithms:
- K-means clustering
- KNN (k-nearest neighbors)
- Hierarchical clustering
- Anomaly detection
- Neural Networks
- Principal Component Analysis
- Independent Component Analysis
- Apriori algorithm
- Singular value decomposition
Some major applications of unsupervised ML algorithms are:
- Clustering automatically devide the dataset into groups based on their similarities.
- Anomaly detection can discover unusual text or data points in your dataset. It is useful for finding fraudulent transactions.
- Association mining identifies sets of items that often occur together in your datapoints/dataset.
- Latent variable models are broadly used for data preprocessing. Like reducing the amount of features in a dataset or decomposing the dataset into multiple components
Challenges in Implementation
In addition to the regular issues of searching the appropriate algorithms & hardware, Unsupervised Learning Algorithms presents a unique challenge: it’s hard to figure out if you’re getting the basic job done or not.
In Supervised Learning Algorithms, we define metrics that drive decision-making around model tuning. Measures like precision & recall give a sense of how accurate your model. Parameters of that technique are tweaked to enhance those accuracy scores. Low accuracy scores clearly mean you need to improve, & so on.
Since there are no labels in the Unsupervised Learning Algorithms, it’s nearly impossible to get a fairly objective measure of how accurate your ML algorithm is. In clustering, let’s say, how can you know if K-Means found the accurate clusters? Are you using the right number of clusters in the first place? We can look to a precision score; here, you need to get a little more creative.
A great part of the “will Unsupervised Learning Algorithm work for me?” question is dependent on your business context. In our example of visitor/customer segmentation, clustering will only work well if your customers fit into natural groups.
One of the best (but most risky) methods to test your unsupervised learning model is implementing it in the real world & seeing what happens! Designing an A/B test–with & without the clusters your algorithm output–can be an effective way to see if it’s useful information or incorrect.
The ML algorithms tasks are broadly classified into Supervised, Unsupervised, Semi-Supervised & Reinforcement Learning tasks.
Unsupervised Learning Algorithms take place without the help of a supervisor. The input data fed to the ML algorithms are unlabelled data, i.e., no output is known for every input. The algorithm finds out the trends & patterns in the input data & creates an association between the input’s different attributes.
In the supervised learning model, Algorithms are trained using labelled data, while in the Unsupervised Learning model, Algorithms are used against unlabelled data.
Unsupervised learning is useful for finding pattern recognition in data, creating clusters of data, & real-time analysis.
Tasks like clustering, KNN algorithms, etc., come under Unsupervised Learning Algorithms. However, Unsupervised learning’s biggest drawback is that you cannot get precise information regarding data sorting.
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