What's Supervised Learning? What's are Essential Topics in Supervised Learning to Become an Artificial Intelligence Expert? Explore More Possibilities from Emerging Perspectives!
Abstract:
Supervised Learning is the process of teaching a model by feeding it input data as well as correct output data. This input/output pair is usually referred to as "labeled data." Think of a teacher who, knowing the correct answer, will either reward marks to or take marks from a student based on the correctness of her response to a question.
This blog article includes about supervised learning, types, applications, advantages, disadvantages etc with modern approaches utilised under Supervised Learning
Keywords: Supervised Learning, Models, advantages, disadvantages, emerging
In machine learning and artificial intelligence, supervised learning refers to a class of systems and algorithms that determine a predictive model using data points with known outcomes. The model is learned by training through an appropriate learning algorithm (such as linear regression, random forests, or neural networks) that typically works through some optimization routine to minimize a loss or error function.
Put another way, Supervised Learning is the process of teaching a model by feeding it input data as well as correct output data. This input/output pair is usually referred to as "labeled data." Think of a teacher who, knowing the correct answer, will either reward marks to or take marks from a student based on the correctness of her response to a question.
What are Supervised Learning Models?
Supervised learning is often used to create machine learning models for two types of problems.
Regression -
The model finds outputs that are real variables (number which can have decimals.)
Classification -
The model finds classes in which to place its inputs.
What are the steps of Supervised Learning?
Training, Test, and Validation
Step1: Labeled Training Data
The first step in the supervised learning process is to gather labeled training data. The label is the output and provides feedback for the algorithm.
Step2: Spiliting the labeled training data
Provided enough data is available, the next step is to split this labeled data into three sets: training, testing, and validation. The algorithm uses training set to adjust the model to minimize the error. For example the training set may contain a variety of animal pictures with a label associated to each picture, allowing the algorithm to compare the predicted label with the correct one.
The validation set is disjoint from the training set and allows one to independently measure the progress of the learning algorithm. This measure can be used to determine a cutoff point in the training algorithm to balance the accuracy of the learned model versus overfitting.
Step 3: Optimal on the validation set
The test set is the final set and it is meant to be used only when the model has been found to be optimal on the validation set. This set provides a ‘real world’ evaluation of the model’s performance on never-before-seen data. Test data is a kind of ‘final exam’ for a model which has learned its training data effectively and can generalize to new data.
Example:
Suppose you are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all the different fruits one by one like this:
If the shape of the object is rounded and has a depression at the top, is red in color, then it will be labeled as –Apple.
If the shape of the object is a long curving cylinder having Green-Yellow color, then it will be labeled as –Banana.
Now suppose after training the data, you have given a new separate fruit, say Banana from the basket, and asked to identify it.
Since the machine has already learned the things from previous data and this time has to use it wisely. It will first classify the fruit with its shape and color and would confirm the fruit name as BANANA and put it in the Banana category.
Thus the machine learns the things from training data(basket containing fruits) and then applies the knowledge to test data(new fruit).
Email Filtering
Supervised learning is commonly used in email filtering to classify incoming emails as spam or legitimate. A machine learning algorithm is trained using a labeled dataset containing examples of both spam and legitimate emails. The algorithm then extracts relevant information from each email, such as the sender’s information, the subject, the message body, and so on. It learns from the labeled dataset to identify patterns and relationships between these features and their corresponding labels (spam or legitimate). Once trained, the algorithm can use the extracted features to predict the label of new, unseen emails. If an email is predicted to be spam, it can be automatically filtered into a spam folder, saving the user’s inbox space.
What are the classification of Supervised Learning?
Supervised learning is classified into two categories of algorithms:
Classification:
A classification problem is when the output variable is a category, such as “Red” or “blue” , “disease” or “no disease”.
Regression:
A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
Supervised learning deals with or learns with “labeled” data. This implies that some data is already tagged with the correct answer.
What are the Types of Supervised Learning?
Regression
Logistic Regression
Classification
Naive Bayes Classifiers
K-NN (k nearest neighbors)
Decision Trees
Support Vector Machine
What are the Types of Supervised Learning?
Regression
Regression is a supervised learning method for determining the relationship between dependent and independent variables. In addition, it employs labeled datasets in an algorithm to forecast continuous output for various data. Here, it is widely used in situations where the output must be a single value, such as weight or height.
There are two types of regression:
- Linear regression: This is used to detect the relationship between two variables and to make future predictions. It is further subdivided according to the number of independent and dependent variables. Simple linear regression, for example, is used when there is only one independent and one dependent variable. Multiple linear regression is used when there are two or more independent and dependent variables.
- Logistic regression: Logistic regression is used when the dependent variable is categorical or has binary outputs such as ‘yes’ or ‘no’. Since logistic regression is used to solve binary classification problems, it predicts discrete values for variables.
Naive Bayes
The Naive Bayes algorithm is well-suited for large datasets because each program in the algorithm operates independently, and the presence of one feature has no effect on the other. Its applications include text classification, and recommendation systems, among others. There are various Naive Bayes models of which the decision tree is commonly used in business.
Classification
Classification is a type of supervised learning algorithm which involves the process of accurately assigning data to different categories or classes. In essence, it entails identifying and analyzing specific entities in order to determine the appropriate category or class. K-nearest neighbor, Random forest, Support vector machines, Decision trees, and Linear classifiers are some popular classification algorithms.
Neutral Networks
Neutral Networks perform the process of grouping or categorizing raw data. Additionally, this algorithm is also employed in the interpretation of sensory data and the identification of patterns. The algorithm’s use, however, is limited due to the need for high computational resources.
Random Forest
The random forest algorithm is known as an ensemble method as it combines multiple supervised learning techniques to make a conclusion. Moreover, it uses several decision trees to classify each tree, making it a popular choice in a variety of industries.
What's the Advantages of Supervised Learning?
The following are the main advantages of Supervised Learning
1. Allows collecting data and produces data output
Supervised learning allows collecting data and produces data output from previous experiences.
2. Optimize performance criteria
Helps to optimize performance criteria with the help of experience.
3. Solve real-world computation problems.
Supervised machine learning helps to solve various types of real-world computation problems.
4. Performing Tasks
It performs classification and regression tasks.
5. Estimating or mapping the result
It allows estimating or mapping the result to a new sample.
We have complete control over choosing the number of classes we want in the training data.
What's the Disdvantages of Supervised Learning?
The disadvantages of Supervised Learning are the following
1. Difficult in classification
Classifying big data can be challenging.
2. Needs a lot of computation time.
Training for supervised learning needs a lot of computation time. So, it requires a lot of time.
3. Cannot handle complex tasks.
Supervised learning cannot handle all complex tasks in Machine Learning.
4. Vast computation time
Computation time is vast for supervised learning.
5. Labelled data set requirements
It requires a labelled data set.
6.Training process
It requires a training process.
Conclusions
Machine learning is quickly growing field in computer science. It has applications in nearly every other field of study and is already being implemented commercially because machine learning can solve problems too difficult or time consuming for humans to solve. To describe machine learning in general terms, a variety models are used to learn patterns in data and make accurate predictions based on the patterns it observes.
When choosing a supervised learning algorithm, there are a few things that should be considered. The first is the bias and variance that exist within the algorithm, as there is a fine line between being flexible enough and too flexible. Another is the complexity of the model or function that the system is trying to learn. As noted, the heterogeneity, accuracy, redundancy and linearity of the data should also be analyzed before choosing an algorithm.
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