What Are the Different Types of Machine Learning Algorithms?
There are different ways of how a machine learns. In some cases, we train them and, in some other cases, machines learn by their own. Well, primarily, there are two types of machine learning – Supervised Learning and Unsupervised Learning. In this module, we are going to discuss the types of machine learning in detail.
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In case you want to jump right into a particular topic, here’s the table of contents for this module:
- Supervised Machine Learning Algorithm
- Unsupervised Machine Learning Algorithm
Without further ado, let’s get started.
The type of learning algorithm where the input and the desired output are provided is known as the Supervised Learning Algorithm. In Supervised Machine Learning, labeled data is used to train machines in order to make them learn and establish relationships between given inputs and outputs. Now, you must be wondering what labeled data means, right? Well, a label is nothing but a known description or a tag given to objects in the data. For instance, you have a dataset that consists of information related to 10 different patients with respective symptoms and their cancer test results. Based on the test results, you can put a tag on each patient specifying whether they are cancer positive or cancer negative.
So, labels in a given data also provide the structure of the algorithm output, that is, any result must be one of these labels. Now comes another question, how does labeled data help in this algorithm? Machines, to learn the patterns, classify this data and apply these patterns to classify new data. You can also put it in this way, when you have labeled input data and you know what needs to be predicted, then you can use Supervised Machine Learning.
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How Does Supervised Machine Learning Algorithm Work?
Now that we are familiar with what Supervised Machine Learning Algorithm means, let us explore how it works.
Step 1: The very first step of Supervised Machine Learning is to load labeled data into the system. This step is a bit time consuming, because the preparation of labeled data is often done by a human trainer. Here, the dataset is divided into train and test sets for further operations.
Step 2: The next step is to train and build connections of inputs and outputs. This step is also known as the training model.
Step 3: Then comes the step known as the testing model. As the name suggests, you test the model by introducing it to a set of new data.
Let us understand this with the help of an example. Suppose, we have a labeled dataset that consists of images of cats and dogs, with different attributes such as nose, tongue, ear etc. Now, we are going to divide this labeled dataset into train and test sets.
The image of a dog shown above has labels such as ears, nose, tongue, and dog. We train the model with this image. Then, we repeat the same training process with other images of both cats and dogs with their attributes. Once the model is trained and the algorithm is built, the accuracy can be tested with the help of a test dataset. When we feed the model with a new dog image, it scans the image and matches the attributes of the image with other trained images. Then depending upon the accuracy of the model, it returns the output ‘dog.’
Now, remember that it takes a large amount of data to build a model with a good accuracy percentage. That is one drawback of this type of algorithms. Let us discuss some positive and negative sides of the Supervised Machine Learning Algorithm.
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Pros and Cons of Supervised Machine Learning
As you might have noticed, in Supervised Machine Learning, the objective is very clear. For example, we want to predict whether the animal in a particular image is a dog or a cat. The training of the machine is tightly controlled, which in return gives an outcome of a very specific behavior. Also, the accuracy of these models can be measured easily.
|Clear objective||Intensive labor|
|Easy-to-measure accuracy||Needs large amount of data|
|controlled training process||Limited insights|
On the other hand, it often becomes labor-intensive as the data requires labeling before the model is trained, which can take hours of human effort. The cost becomes astronomical then and the training process gets slowed down. After preprocessing the data, we might have to eliminate useless data. This might limit the data that the system can work with.
Another drawback of this algorithm is that we limit the insight for a machine to explore, as the predicted behavior is mentioned in advance. There is no freedom for the machine to explore other possibilities, unlike in Unsupervised Machine Learning.
Types of Supervised Machine Learning Algorithm
Supervised Machine learning has primarily two types of Machine Learning algorithms. They are classification in machine learning and regression in machine learning.
|Classification||To predict a categorical result|
|Regression||To find the relationship between variables|
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Classification in Machine Learning
Classification in machine learning algorithm classifies the input data into one of several predefined classes. Classification in machine algorithm is useful for providing categorical outcomes that fit within the predefined labels.
Classification in Machine Learning Use Cases
Credit card fraud detection and email spam detection are the use cases of binary classification. There are only two possible output values in this type of algorithms. They can be either fraud or not fraud and spam or non-spam.
But if the question we are asking does not have potential categorical answers, then we aren’t dealing with a problem of classification in machine learning, it is more of a problem that falls under regression in machine learning. Let us discuss what regression in machine learning is.
Regression in Machine Learning
It is a predictive algorithm that attempts to predict the output value when the input value is given. It deals with continuous numerical values. It estimates the relationship between variables.
Regression in Machine Learning Use Cases
This type of algorithm can be used to determine if a customer is going to churn or not, depending upon the customers behavior. It can also be used to predict housing price models.
Regression in Machine Learning defines the strength of correlation between two attributes, which allows us to find a predictive range of likelihood.
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Supervised Machine Learning Algorithm Table
It is very important to understand which Machine Learning algorithm should be applied depending upon the type of problem we are dealing with. The below table consists of different types of Machine Learning problems and the possible algorithms for solving those problems.
As we have seen that Supervised Machine Learning deals with known labeled data for prediction, you must be thinking what if we don’t have labeled data? How to apply learning algorithms for unlabeled data? Well, for that we have Unsupervised Machine Learning. Let us explore the next types of machine learning, that is, Unsupervised Learning.
Unsupervised Machine Learning
Unsupervised Machine Machine Learning type of Machine Learning models, we don’t have labeled data. Since we are not aware of the predefined outcome, there are certain questions that are left hanging for us to wonder: How to find the underlying structure of a given dataset? How to summarize it or group it usefully? In a way, these can be considered as primary goals of this type of Machine Learning. Since there is no specific outcome or target to predict, this Machine Learning type is called ‘Unsupervised Machine Learning.’
When we don’t know how to classify the given data but we want the machine to group or classify it for us, use this Machine Learning technique. Now, let us try to understand how Unsupervised Machine Learning works.
How Does Unsupervised Machine Learning Works?
When the data given is not labeled, the following steps are followed in order to learn and gain insights:
Step 1: The very first step is to load the unlabeled data into the system.
Step 2: Once the data is loaded into the system, the algorithm analyzes the data.
Step 3: As the analysis gets completed, the algorithm will look for patterns depending upon the behavior or attributes of the dataset.
Step 4: Once pattern identification and grouping are done, it gives the output.
Pros and Cons of Unsupervised Machine Learning
Not having labeled data turns out to be good in some cases. Unsupervised Machine Learning techniques are much faster to implement compared to Supervised Machine Learning, since no data labeling is required here. That is, fewer human resources is required in order to perform tasks. This algorithm has the potential to provide unique, disruptive insights for a business to consider as it interprets data on its own.
But on the downside, in Unsupervised Machine Learning, it is not easy to measure the accuracy since we don’t have any expected or desired outcome to compare to. Sometimes, it requires more tuning in order to get meaningful results. Also, it does not naturally deal with high-dimensional data. When the dimension of data and the number of variables become more and need to be reduced in order to work on that data, then the human involvement becomes necessary to clean the data.
Types of Unsupervised Machine Learning Algorithms
Primarily, there two categories in Unsupervised Machine Learning: Clustering and Dimensionality Reduction.
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Clustering in Machine Learning
Various types of Machine Learning algorithms include clustering algorithm, which runs through the given data to find natural clusters if they exist. There are a few different clustering techniques but remember that any clustering algorithm will typically output all of the data points in their respective clusters. Now, it is totally up to us to decide what they actually mean and exactly what the algorithm has found.
There are different types of machine learning clustering techniques available. They are:
- K-Means Clustering: Clustering the data points into k number of exclusive clusters
- Hierarchical Clustering: Clustering the data points into parent and child clusters
- Probabilistic Clustering: Clustering the data points into clusters on a probabilistic scale
Dimensionality Reduction in Machine Learning
- Dimensionality reduction is one of the most important Machine Learning types. Since we don’t have predefined outcomes or results in the case of Unsupervised Machine Learning, measuring accuracy of the model becomes difficult. In order to make a model that is closer to accurate or desired, it is preferred to keep the data precise in the first place. Sometimes data becomes very large or the number of variables becomes more. That is when the dimensionality reduction method comes into the picture: here, ‘dimension’ refers to the number of columns present in the dataset.How does it perform the reduction process? Well, most common dimensionality reduction techniques aim to find some ‘hyperplane,’ which is nothing but a higher dimensional version of a line. Let us see some of the most commonly used dimensionality reduction algorithms:
Principal Component Analysis (PCA): The algorithm selects a hyperplane such that when all points are projected onto the plane, they are maximally spread out.
- Singular-value Decomposition (SVD): This algorithm factorizes the data into the product of three different smaller matrices.
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What Did We Learn so Far?
This module highlighted the primary machine learning types, their workings, subcategories, regression in machine learning, classification in machine learning, clustering in machine learning, dimensionality reduction in machine learning, their use cases, and the pros and cons of different types of Machine learning. In the next module, we will be discussing different aspects related to datasets for Machine Learning. See you there.