Pros And Cons Of Supervised Machine Learning

While doing a project recently, I wondered what the advantages and disadvantages of supervised machine learning are. I’ve done a bit of research on the subject, and I think you might find it interesting.

If you don’t have much time and then here is a quick answer:

Supervised learning has many advantages, such as clarity of data and ease of training. It also has several disadvantages, such as the inability to learn by itself.

Pros and Cons of Supervised Machine Learning

If you came here to spend some time and really look into the pros and cons of supervised machine learning, then let’s dive in.

Pros of Supervised Machine Learning

  • You will have an exact idea about the classes in the training data.
  • Supervised learning is a simple process for you to understand. In the case of unsupervised learning, we don’t easily understand what is happening inside the machine, how it is learning, etc.
  • You can find out exactly how many classes are there before giving the data for training.
  • It is possible for you to be very specific about the definition of the classes, that is, you can train the classifier in a way which has a perfect decision boundary to distinguish different classes accurately.
  • After the entire training is completed, you don’t necessarily need to keep the training data in your memory. Instead, you can keep the decision boundary as a mathematical formula.
  • Supervised learning can be very helpful in classification problems.
  • Another typical task of supervised machine learning is to predict a numerical target value from some given data and labels.

I hope you’ve understood the advantages of supervised machine learning. Now, let us take a look at the disadvantages. There are plenty of cons. Some of them are given below.

Cons of Supervised Machine Learning

  • Supervised learning is limited in a variety of sense so that it can’t handle some of the complex tasks in machine learning.
  • Supervised learning cannot give you unknown information from the training data like unsupervised learning do.
  • It cannot cluster or classify data by discovering its features on its own, unlike unsupervised learning.
  • In the case of classification, if we give an input that is not from any of the classes in the training data, then the output may be a wrong class label. For example, let’s say you trained an image classifier with cats and dogs data. Then if you give the image of a giraffe, the output may be either cat or dog, which is not correct.
  • Similarly, let’s say your training set does not include some examples that you want to have in a class. Then, when you use those examples after training, you might not get the correct class label as the output.
  • While you are training the classifier, you need to select a lot of good examples from each class. Otherwise, the accuracy of your model will be very less. This is difficult when you deal with a large amount of training data.
  • Usually, training needs a lot of computation time, so do the classification, especially if the data set is very large. This will test your machine’s efficiency and your patience as well.
  • We can not always give lots of information with supervision. A lot of the time, the machine needs to learn by itself from the training data. As Geoffrey Hinton quoted in 1996,there’s only one place you can get so much information, that is,  from the input itself “.

As you can see, there are a lot of advantages as well as disadvantages of supervised machine learning in general. But most of the time, the pros and cons of supervised learning depend on what supervised learning algorithm you use.

Supervised Learning vs Unsupervised Learning

Machine learning systems are classified into supervised and unsupervised learning based on the amount and type of supervision they get during the training process. 

In supervised learning, the training data includes some labels as well. That means we are providing some additional information about the data.

For example, if we are training an image classifier to classify dogs and cats, then we will tell the machine something like ‘These are all the images of cats and these are the images of dogs. Now, learn from these.’  That means we will give the additional label for each image in our training data set, either dog or cat.

Supervised Learning Algorithms

Here are some of the most commonly used supervised machine learning algorithms out there.

  1. Linear Regression
  2. Logistic Regression
  3. K-Nearest Neighbors
  4. Support Vector Machine (SVM)
  5. Decision Trees
  6. Random Forests
  7. Neural Networks (some may be unsupervised as well)

In the case of unsupervised learning, the training data that we give to the machine is unlabeled. For example, if you want to do grouping or clustering of some data that you don’t know much about, then, in that case, unsupervised learning will be useful.

Another situation where unsupervised learning will be useful is error detection or anomaly detection. Unsupervised learning can be used to extract some unknown information from the data.

Unsupervised Learning Algorithms

Here are some of the most commonly used unsupervised machine learning algorithms.

  1. K-means
  2. Hierarchical Cluster Analysis (HCA)
  3. Expectation Maximization
  4.  Locally-Linear Embedding (LLE)
  5. Kernel PCA
  6. Principal Component Analysis
  7. t-distributed Stochastic Neighbor Embedding (t-SNE)
  8. Apriori
  9. Eclat

What is Semisupervised Learning?

Semisupervised learning can be called as a mixture of supervised learning and unsupervised learning. Some machine learning algorithms can deal with partially labeled data. Most of the time, more of the data is unlabeled, and some data is labeled.

Facebook is an example of this type of learning. For example, once you upload some photos of you along with your friends, Facebook automatically recognizes that the same person (your friend) shows up in some other photos as well.

In this case, Facebook only needs some labels, maybe one label per person, and it is able to label everyone in all other photos as well.

Most semisupervised learning algorithms are combinations of unsupervised and supervised algorithms.

Many machine learning researchers have made it clear that unlabeled data, when used together with a small amount of labeled data, can produce a large amount of improvement in accuracy of learning over unsupervised learning. But it does not require the time and costs needed for supervised learning.

One example of semi-supervised learning algorithms is Deep Belief Networks (DBNs). DBN is a class of deep neural network which consists of multiple layers of the graphical model having both directed as well as undirected edges.

What is Reinforcement Learning?

Reinforcement learning is pretty different from all the other mentioned methods. In this type of machine learning, the machine learns by itself after making several mistakes.

From all the mistakes made, the machine can understand what the causes were, and it will try to avoid those mistakes again and again. Reinforcement learning is also known as the trial and error way of learning.

This is how human beings learn. Take the case of small babies. If they touch fire by accident or knowingly, they will feel the pain, and they will never touch fire again in their entire life unless it is an accident.

In this context, the learning system is referred to as an agent. This system must learn by itself, which is the best strategy, known as a policy, to get the most positive reward over time.

Generally, reinforcement learning contains six steps.

  1.  Observe
  2. Select an action using the policy
  3. Do the action
  4. Get positive or negative rewards
  5. Update the policy by analyzing the rewards
  6. Repeat the same process until an optimal policy is obtained.

Many robots learn how to walk by implementing reinforcement learning. This is what human babies also do. They will try to walk desperately, just to fall to the ground at first.

Then, they will try more and more. Finally, they will learn the skill perfectly, and they will never forget how to walk in their entire life. Just like that, robots also learn how to walk perfectly, using reinforcement learning algorithms.

Reinforcement learning has several applications in the real world. It is not the perfect way of learning things. But this is the feature that stands out for reinforcement learning, which is it’s the biggest advantage as well.

Which is the Best Machine Learning Strategy?

There are typically four kinds of machine learning strategies available that we can use to train the machine, specifically, supervised learning, unsupervised learning, semi-supervised learning, and finally, reinforcement learning.

Out of these, which one is the better strategy? Well, it depends on what your goal is and what type of algorithm you are using. There are various types of algorithms available under all these four strategies, and we can’t tell which one is the best of them.

For example, there are some algorithms suitable for image classification. Some of them will be very useful for clustering. Some of the algorithms may be perfect for visualization, finding associations, predicting numerical results, etc. Each algorithm has its own purpose.

Each algorithm performs differently for different operations, and we need to choose the right algorithm for the right kind of application. Choosing the right kind of algorithm will affect your results in either good or bad ways. So, always do some research before selecting a suitable algorithm for your project.

If you are a beginner in machine learning, I highly recommend you check out this article, which is a beginner’s guide to machine learning.

If you have any queries regarding machine learning or deep learning with Python, feel free to let me know them in the comments section.

Do you find this article useful? If so, share it with your friends.

Happy Learning 🙂

Ashwin Joy

I'm the face behind Pythonista Planet. I learned my first programming language back in 2015. Ever since then, I've been learning programming and immersing myself in technology. On this site, I share everything that I've learned about computer programming.

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