机器学习

As the name suggests, unsupervised learning is a machine learning technique in which models
are not supervised using training dataset. Instead, models itself find the hidden patterns and insights
from the given data. It can be compared to learning which takes place in the human brain while learning
new things. It can be defined as:
“Unsupervised learning is a type of machine learning in which models are trained using
unlabeled dataset and are allowed to act on that data without any supervision.”
Unsupervised learning cannot be directly applied to a regression or classification problem
because unlike supervised learning, we have the input data but no corresponding output data. The goal
of unsupervised learning is to find the underlying structure of dataset, group that data according to
similarities, and represent that dataset in a compressed format
Example: Suppose the unsupervised learning algorithm is given an input dataset containing images of
different types of cats and dogs. The algorithm is never trained upon the given dataset, which means it
does not have any idea about the features of the dataset. The task of the unsupervised learning
algorithm is to identify the image features on their own. Unsupervised learning algorithm will perform
this task by clustering the image dataset into the groups according to similarities between images.
Why use Unsupervised Learning?
Below are some main reasons which describe the importance of Unsupervised Learning:
o Unsupervised learning is helpful for finding useful insights from the data.
o
 Unsupervised learning is much similar as a human learns to think by their own experiences,
which makes it closer to the real AI.
o
 Unsupervised learning works on unlabeled and uncategorized data which make unsupervised
learning more important.
o
 In real-world, we do not always have input data with the corresponding output so to solve such
cases, we need unsupervised learning.
Working of Unsupervised Learning
Working of unsupervised learning can be understood by the below diagram:
Here, we have taken an unlabeled input data, which means it is not categorized and
corresponding outputs are also not given. Now, this unlabeled input data is fed to the machine learning
model in order to train it. Firstly, it will interpret the raw data to find the hidden patterns from the data
and then will apply suitable algorithms such as k-means clustering, Decision tree, etc.
Once it applies the suitable algorithm, the algorithm divides the data objects into groups according to
the similarities and difference between the objects.
Types of Unsupervised Learning Algorithm:
The unsupervised learning algorithm can be further categorized into two types of problems:
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o
Clustering: Clustering is a method of grouping the objects into clusters such that objects with
most similarities remains into a group and has less or no similarities with the objects of another
group. Cluster analysis finds the commonalities between the data objects and categorizes them
as per the presence and absence of those commonalities.
o
Association: An association rule is an unsupervised learning method which is used for finding
the relationships between variables in the large database. It determines the set of items that
occurs together in the dataset. Association rule makes marketing strategy more effective. Such
as people who buy X item (suppose a bread) are also tend to purchase Y (Butter/Jam) item. A
typical example of Association rule is Market Basket Analysis.
Unsupervised Learning algorithms:
Below is the list of some popular unsupervised learning algorithms:
o K-means clustering
o
 KNN (k-nearest neighbors)
o
 Hierarchal clustering
o
 Anomaly detection
o
 Neural Networks
o
 Principle Component Analysis
o
 Independent Component Analysis
o
 Apriori algorithm
o
 Singular value decomposition
Advantages of Unsupervised Learning
o Unsupervised learning is used for more complex tasks as compared to supervised learning
because, in unsupervised learning, we don't have labeled input data.
o
 Unsupervised learning is preferable as it is easy to get unlabeled data in comparison to labeled
data.
Disadvantages of Unsupervised Learning
o Unsupervised learning is intrinsically more difficult than supervised learning as it does not have
corresponding output.
o
 The result of the unsupervised learning algorithm might be less accurate as input data is not
labeled, and algorithms do not know the exact output in advance.
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Supervised Learning
 Unsupervised Learning
Supervised learning algorithms are trained using labeled data.
 Unsupervised learning algorithms are trained using unlabeled data.
Supervised learning model takes direct feedback to check if it is predicting
 Unsupervised learning model does not take any feedback.
correct output or not.
Supervised learning model predicts the output.
 Unsupervised learning model finds the hidden patterns in data.
In supervised learning, input data is provided to the model along with the
 In unsupervised learning, only input data is provided to the model.
output.
The goal of supervised learning is to train the model so that it can predict
 The goal of unsupervised learning is to find the hidden patterns and
the output when it is given new data.
 useful insights from the unknown dataset.
Supervised learning needs supervision to train the model.
 Unsupervised learning does not need any supervision to train the
model.
Supervised learning can be categorized
 Unsupervised Learning can be classified
in Classification and Regression problems.
 in Clustering and Associations problems.
Supervised learning can be used for those cases where we know the input
 Unsupervised learning can be used for those cases where we have
as well as corresponding outputs.
 only input data and no corresponding output data.
Supervised learning model produces an accurate result.
 Unsupervised learning model may give less accurate result as
compared to supervised learning.
Supervised learning is not close to true Artificial intelligence as in this, we
 Unsupervised learning is more close to the true Artificial
first train the model for each data, and then only it can predict the correct
 Intelligence as it learns similarly as a child learns daily routine
output.
 things by his experiences.
It includes various algorithms such as Linear Regression, Logistic
 It includes various algorithms such as Clustering, KNN, and Apriori
Regression, Support Vector Machine, Multi-class Classification, Decision
 algorithm.
tree, Bayesian Logic, etc.