Supervised Machine Learning – This is our first post in this subseries “Machine Learning Type” under master series “Machine Learning Explained“. We will only talk about supervised machine learning in details here. Machine learning algorithms “learn” from the observations. When exposed to more observations, the algorithm improves its predictive performance.
Supervised Machine Learning is a type of system in which both input and desired output data are provided.
Machine Learning for Businesses
Supervised Learning is becoming a good friend for a marketing business in particular. For example how much money will we make by spending more dollars on digital advertising? Or even making small predictions for stock markets i.e. What’s going to happen to the stock market tomorrow?
Machine learning techniques are accelerating almost on a daily basis with intentions to bring good values to the businesses of today. It is revolutionising the way we do our business and what should be done to improve upon. On a high level, we got three main types of Machine Learning types i.e. Supervised, Unsupervised and Reinforcement learning. Since this post is limited to supervised learning and what it is doing in business; so I will stick to it only for now.
Supervised Machine Learning -SML
Let’s understand a bit about SML and find answers around what it does, how it does, and what it can do for our real-life business. Supervised learning through historical data set is able to hunt for correct answers, and the task of the algorithm is to find them in the new data.
How it’s powering our businesses to make sure we survive and get the best out of what we do. Supervised Machine Learning is
- Is a task of deducing function from labelled training data.
- Making predictions based on evidence in the presence of uncertainty
- Identifying patterns in given data with adaptive algorithms
Machine learning techniques are accelerating daily basis with intentions to bring good values to the businesses of today.
In supervised machine learning, each example is sorted in a pair consisting of an input object and the desired output value. An algorithm in this domain analyses the training data and produces an inferred function, which can be used for mapping new examples. The training data consist of a set of training examples. This learning uses techniques as below to develop predictive models.
- Regression – To predict the output value using training data.
- Classification – To group the output into a class.
SML is a type of system in which both input and desired output data are provided. Input and output data are labelled for classification to provide a learning basis for future data processing. Here I am going to describe an empirical description of supervised machine learning techniques.
Frequently used Algorithms in Supervised Machine Learning
Supervised learning gives a glimpse of how to solve classification and regression problems. The algorithm is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data. It has “labelled” data for creating predictive models by using either type of ml algorithms as mentioned below. It provides outputs typically in one of two forms.
- Regression outputs are real-valued numbers that exist in a continuous space.
- Classification outputs, on the other hand, fall into discrete categories.
We will not define the algorithms mentioned in the above picture, These were defined in our previous post. Also in my upcoming posts, we will try to take each one of them in a very detailed manner including their definition, use cases and flow etc. For now, let’s just keep our focus on supervised learning where each example is a pair consisting of an input object i.e. typically a vector and desired output value.
Supervised Learning Process – SLP
While there are many Statistics and Machine Learning algorithms for supervised learning, most use the same basic workflow for obtaining a predictor model. The accuracy of SLP is determined by the number of correct classification divided by the total number of test cases. This equation clearly shows accuracy will be more close to perfection when we have when the difference between “number of correct classification” and “number of test cases” is minimal.
The process for Supervised Machine Learning is basically a two-step process as below.
- Learning – Learn a model using the training data or train model using training data.
- Testing – Test the model using unseen test data to assess the model accuracy
- Prepare data
- Choose an algorithm
- Fit a model
- Choose a validation method
- Examine fit and update until satisfied
- Use the fitted model for predictions
The detailed steps for supervised learning processes are included but not limited though with pointers as above.
Goals of Supervised Learning
The goal of supervised learning is to approximate the mapping function well. An algorithm is used to learn the mapping function from the input to the output. In other words, when input data (X) comes in predictions can be made for expected output variables (Y) for that data. Learning stops when the algorithm achieves an acceptable level of performance. This functional mapping takes the general form y = f(x) — specified target output y, provided inputs x, and the ML algorithm will learn the optimal f() by finding patterns in the data.
Y = f(X)
When an algorithm is applied on input variables (X) and an output variable (Y) to learn the mapping function from the input to the output which tells us that probably a supervised machine learning used here. The processor task is called supervised learning because an algorithm learning from the training data set can be thought of as a supervisor supervising the learning process.
Use cases of Supervised Learning- Real Business
Supervised Learning is as good as low hanging fruit in data science for businesses. The key question when dealing with ML classification is not whether a learning algorithm is superior to others, but under which conditions a particular method can definitely outperform others on a given problem.
Supervised learning model makes predictions based on evidence in the presence of uncertainty. Some of the use cases for supervised learnings are depicted in the below picture.
Points to Note:
All credits if any remains on the original contributor only. We have covered supervised machine learning where we make predictions from labelled historical data. In the next upcoming post will talk about unsupervised machine learning.
Books + Other readings Referred
- Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
- Lab and hands-on experience of @AILabPage (Self-taught learners group) members.
Feedback & Further Question
Do you have any questions about AI, Machine Learning, Data Science or Big Data Analytics? Leave a question in a comment or ask via email. Will try best to answer it.
Conclusion – Supervised Learning which is one of three types of machine learning. This post is limited to supervised learning in order to explore its details i.e. what it is doing and can do for businesses as new electricity to power them up. This blog post I tried to performed a comparison of different supervised machine learning techniques in classifying FinTech data. This blog post is an attempt to describes the best-known supervised techniques in relative detail but not to claim anything. The aim was to produce a lighter rephrase of supervised learning and review of the key ideas rather than a simple list of all algorithms in this category.
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