A Beginners Guide to Supervised Machine Learning
Machine learning is a subset of artificial intelligence that allows machines to detect data patterns and develop problem-solving models without leveraging definitive programming. Machine learning refers to a training process where the algorithm identifies data patterns and uses those patterns to nip the model and provide accurate outcomes every time. Machine learning has been divided into three subcategories: supervised, unsupervised, and reinforcement learning.
In this blog, we will learn all about Supervised Learning. So, let’s get started.
What is Supervised Machine Learning?
Supervised machine learning or supervised learning is a subcategory of artificial intelligence and machine learning. In supervised learning, we train a machine using well-labelled data to predict accurate outcomes or classify data. It is an algorithm of machine learning that is designed to learn by example. We call this process "supervised machine learning" because it will be similar to having a teacher supervise the entire process.
In supervised learning, each example is a pair of input objects and a required output value. The supervised learning model is trained until it can detect the underlying relationships and patterns between the input and output data labels, allowing it to offer accurate labelling whenever presented with a new set of data. The supervised learning algorithm analyzes the training data and delivers an implied function that can be used to map new examples. With supervised learning, an organization can solve diverse problems including classifying spam mails, image classification, fraud detection and risk assessment.
How Supervised Machine Learning Works?
Supervised Machine Learning uses a training set to teach models to provide the required result. It needs a set of input and output to learn from and develop a predictive model. Supervised learning algorithms adapt based on a tuning parameter model set whose outputs best match the inputs of the models. In supervised learning, the goal is to create a y = f(x) form model to predict outcomes, y, from inputs, x. The accuracy of the result is measured by a loss function, adjusting till the errors have been minimized.
Supervised learning is categorized into two major problem algorithms, classification and regression.
Classification: The classification uses an algorithm to predict categorical or class outputs. It aims to sort inputs into a given number of classes or categories depending upon the data label it was trained on. It determines whether a certain transaction is a fraud or not, an email is spam or not, etc. Some of the popular classification algorithms include Decision Trees, Linear Classifiers, Random Forest, Support Vector Machines, and K-Nearest Neighbor.
Regression: Regression is a predictive statistical process in which supervised machine learning models strive to establish a connection between dependent and independent variables. The regression algorithm aims to find a continuous number like income, sales or test scores. The most commonly used regression algorithms are Logistic Regression, Linear Regression, Bayesian Linear Regression, and Polynomial Regression.
Algorithms Used for Supervised Machine Learning
As mentioned in the previous section, supervised machine learning has multiple algorithms that are used for supervised machine learning processes. So, below are explanations of the most commonly used ones to help you understand how these algorithms work.
Linear regression is used to determine the relationship between independent and dependent variables to make predictions about the possible outcomes in the future. When the number of independent and dependent variables are one, it is called simple linear regression. However, as the number of variables increases, it is known as multiple linear regression. A linear line that represents the relationship between two variables (either positive or negative) in a graph is called the regression line. And for each linear regression, it seeks a line plot of best fit that is calculated through least-squares methods.
Unlike linear regression that is used for continuous dependent values, logistic regression is selected when the dependent variable is categorical, i.e., “yes” or “no” and “true” or “false”. Logistic regression is majorly used to solve binary classification problems like spam identification.
K-Nearest Neighbor or K-NN is the simplest algorithm to be used in supervised learning. It is a non-parametric algorithm (i.e. it does not make presumptions on underlying data) that categorizes data points according to the association and proximity to other available data. The K-NN algorithm just stores the data at the training phase and only at the time of classification, it acts and classify that data into the accurate category. Hence, getting its another name: lazy learner algorithm. KNN algorithm is mainly used for image recognition and recommendation engine.
Random forest is a flexible algorithm of supervised learning that is used for both classification and regression problems. The word “forest” in random forest refers to the collection of uncorrelated decision trees that are merged for reducing variance and generating highly accurate predictions of data. It will handle the accuracy of large data sets and maintain missing values. The random forest algorithm can be used for the classification of a loyal loan applicant, predict diseases and detect fraudulent activities.
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Support Vector Machines
Another algorithm that is used for both regression and classification problems is Support Vector Machines (SVM). SVM aims to create the best line or decision boundary to segregate n-dimensional space into classes. So, we can put new data points into the right category. And this best line or decision boundary is known as a hyperplane that separates the data points classes on either side of the plane at its maximum. It can be used for text categorization, face detection, and image classification.
Challenges, Advantages and Disadvantages of Supervised Machine Learning
Now that we know the working of supervised machine learning along with its algorithms, let’s understand the challenges, advantages and disadvantages supervised learning comes with.
Training data with irrelevant input features could lead to inaccurate results
It is always challenging to prepare and preprocess data.
If you don’t have an expert to train the machine learning model, you can also use the "brute-force" method. This method means choosing the appropriate features (input variables) to train the system with. However, it could be inaccurate.
When impossible, unlikely, or incomplete values are used as training data, the accuracy of the model is lowered.
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With supervised learning, you can easily collect data or create a data output from prior experience.
Supervised learning helps in solving real-world computational problems with ease.
Using experience, supervised learning can optimize performance criteria as well.
Big data can be difficult to classify.
You might overtrain your decision boundary if your training set doesn't contain examples you want in class.
When you train the classifier, you should select lots of good examples from each class.
It takes a lot of computation time to train for supervised learning.
Using supervised learning models, manual data classification in your application can be removed and predictions based on labelled data made. But, training and using supervised machine learning for any project requires human knowledge and expertise. And with the basic knowledge of its working, algorithms, challenges and advantages, you can easily decide whether you want to go with supervised learning.