supervised machine learning
Let’s start talking about supervised learning with an example:
Suppose you have a series of pictures of different animals and you want to separate and classify them into different categories. The first step is to introduce all the animals in the pictures to the device:
If one of them has a small body with long ears and wide claws, the label is rabbit. If one of them has a large body with a long trunk, the label is an elephant. After data education, you give a new images to the device and you want to identify it.
The device behaves intelligently based on what it has learned from previous data, first classifying the image of the animal by its size and appearance, confirming the image, and placing it in the rabbit category. So the machine learns things from educational data (images of different animals) and then uses the knowledge gained to test new data (new images). This algorithm is called supervised machine learning.
Earlier in the article, doing a machine learning project in Bigpro1, we gave a summary of supervised machine learning. In the following sections, we will introduce supervised machine learning, its algorithms and performing a supervised learning project in Bigpro1 in detail.
Performing a supervised machine learning Bigpro1
Nowadays machine learning is a hot topic in universities, as evidenced by machine learning projects and dissertations. One of the most important machine learning algorithms is supervised machine learning algorithm.
Carrying out supervised machine learning projects due to the use of multiple tools and the need for sufficient expertise to use these tools is a tedious task and requires a lot of time and money. The best way to do a machine learning project is to entrust it to a specialist.
Bigpro1 allows users to perform their supervised machine learning projects with just a few simple clicks and without sufficient expertise in using the required tools, by registering the necessary parameters for these algorithms, and receive the most accurate and complete result.
What is supervised machine learning?
Supervised machine learning is a subset of machine learning and artificial intelligence. Supervised machine learning algorithms are defined using labeled datasets to teach algorithms for data classification or accurate prediction of results. The supervised learning model is trained to the point that it can identify underlying patterns and relationships between input data and output tags, enabling it to provide accurate tagging results when presenting previously unseen data.
Supervised learning helps organizations solve a variety of real-world problems at different scales, such as sorting spams in a separate folder in the Gmail Inbox. Supervised learning is used in classification and regression problems, such as sorting spams in a separate folder in your inbox, determining which category a news article belongs to, or forecasting sales for a given future date.
The success of building, scaling, and accurately applying supervised machine learning models needs highly skilled teams and data scientists, time, and technical skills.
For example, you want to train a machine to help you predict how long it will take you to get home from work; here, you start by creating a collection of tagged data. This data includes: weather conditions, time of day and vacations.
All these details are your input, output is the time it took to return home on that particular day. You know instinctively that if it rains outside, it will take longer to drive home. But the device needs data and statistics.
The first thing you need to create is a training package, which includes the total travel time and related factors such as weather, time and so on. Based on this set of tutorials, your device may see that there is a direct relationship between the amount of rain and the time you will have to take to get home.
Therefore, is assured that the more it rains, the longer you drive to get home. It may also observe the relationship between time to leave work and time to be on the road. The closer you get to 6pm, the longer it takes to get home. Your machine may find some connection to your labeled data. This is the beginning of your data modeling. It shows how rain affects people driving. It is also observed that more people travel at a certain time of the day.
Ensemble learning algorithm in supervised machine learning
Ensemble learning algorithm is a meta-general approach in machine learning that pursues better predictive performance by combining multi-model predictions. Ensemble learning methods are popular when the best performance in a modeling project is the most important predictor of results.
Although there seem to be an infinite number of sets that can be made for a prediction modeling problem, there are three methods that dominate the field of ensemble learning; so much so that each, instead of the algorithm itself, is a context that has created more specialized methods. The three main classes of ensemble learning methods are: bagging, boosting and stacking.
Bagging:
This algorithm is used to improve the stability and accuracy of machine learning algorithms.
Boosting:
Boosting is another method of ensemble learning algorithms.
Stacking:
Its purpose is to predict the best output.
Classification algorithm in supervised machine learning
A supervised learning classification algorithm aims to classify inputs into a certain number of categories or classes, based on the tag data on which they are taught. Classification uses an algorithm to accurately determine test data into specific categories.Identifies these specific entities in the dataset and attempts to conclude how these entities should be labeled or defined. In other words, classification means grouping outputs within a class. If the algorithm tries to label the input in two separate classes, it is called binary classification. Choosing between more than two classes is called multi-class classification.
Classification algorithms can be used for binary classifications such as filtering email into spam or non-spam and classifying customer feedback as positive or negative. Recognizing features, such as identifying handwritten letters and numbers or classifying drugs into many different categories, is another classification problem that can be solved by supervised learning.
Classification algorithms include: linear classifiers, support vector machine (SVM) algorithms, decision trees, K-nearest neighbors, random forest.
Regression algorithm in supervised machine learning
Regression tasks are different because they expect the model to establish a numerical relationship between input and output data. Examples of regression models include forecasting real estate prices based on zip code or predicting click-through rates in online advertising in relation to the time of day or determining the amount of money customers want to pay for a particular product based on their age.
Regression algorithms include: linear regression, logistic regression, neural networks, linear segregation analysis, decision trees, similarity learning, Bayesian logic, random forests, support vector machine (SVM)
The support vector machine is used in both the classification algorithm and the regression algorithm. The goal of the SVM algorithm is to create the best decision line or boundary that can separate the next n space into a class so that in the future the new data point can be categorized correctly. This boundary of the best decision is called the superplane.
Applications of supervised machine learning
Supervised machine learning has many applications in today’s world, some examples of which are:
BioInformatics: This is one of the most well-known supervised learning programs because many of us use it in our daily lives. BioInformatics stores the biological information we humans have, such as fingerprints, iris tissue, earlobes, and more. Today’s cell phones are able to learn our biological information and can then be effective in verifying system security. Smartphones like the iPhone and Google Pixel are able to recognize faces, while OnePlus and Samsung are able to recognize a finger on the screen.
Speech Recognition: This is a program in which you teach your voice algorithm and it can recognize you. The most well-known real-world applications are virtual assistants such as Google Assistant and Siri, which are only opened by the keyword with your voice.
Object-Recognition for Vision: These types of programs are used when you need to identify something. You have a huge data set that you use to train your algorithm and can be used to identify a new instance. Raspberry Pi algorithms that detect objects are the most popular example.
Carry out a supervised machine learning project with Bigpro1
Nowadays, due to the special importance of machine learning and its algorithms such as supervised learning, researchers, business owners and students are looking to do their project in the best possible way.
But as mentioned above, doing a supervised machine learning project by oneself is difficult and time consuming because it requires a lot of tools and their use requires sufficient skills.
Bigpro1 system has well identified all the needs of users in the field of machine learning and therefore has implemented many supervised machine learning algorithms in its system to meet the needs of users accurately and comprehensively.
To perform the supervised machine learning project, users can enter their user dashboard and select the desired algorithm, enter the necessary parameters and submit their project to the Bigpro1 system and wait for the result of their project in the shortest possible time.
Click the button below to start your supervised learning project:
