Supervised Learning: Definition

, Types and Examples

What is Supervised Learning?

What is supervised learning? This is a method in machine learning or machine learning through training models using data with certain outputs or labels. The learning algorithm system uses a dataset with input and output pairs. Each input has a corresponding label.

The existence of supervised learning can help learn the pattern of input-output relationships. The hope is that the model can produce high accuracy in predictions when given new input to the system.

What are the Types of Supervised Learning?

There are at least two types of supervised learning that you need to know. Here are the types:

1. Classification method

Supervised learning can be in the form of classification, this method can be used when the predicted output is a class/category. Later, the algorithm will classify the input into one of the categories from several options. Supervised learning classification can be found in SVM or Support Vector Machine for example.

In real applications, this classification method can be found in the classification of spam messages in emails, facial recognition and so on.

2. Regression method

Unlike classification, supervised learning is a model that is considered when the predicted output has a continuous value. The working system of the regression algorithm is to model the input-output relationship by minimizing the error in its prediction.

Examples are Ridge Regression, Linear Regression and other types of regression. In real practice, we can see this regression, for example, in determining the price of a house. The price is predicted according to certain features such as location, area, number of rooms and other factors.

Characteristics of Supervised Learning

What are the characteristics of supervised learning? Here are some of them:

Model training: Model training relies on the training dataset. This dataset has input-output pairs. Later, the algorithm will try to understand the pattern of the data.

Evaluation by testing: The validation dataset will be used to evaluate the performance of the trained model.

Minimizing the loss function: An algorithm system that minimizes the loss function or loss function around the model’s prediction error.

Output prediction: The trained model is used to make output predictions. This prediction is made based on unknown and new inputs.

Use of labeled data: Labeled data will be used, each input in the dataset has an output that can be known.

Examples of Supervised Learning Implementation

In my opinion, the use of supervised learning is very common. Its ability to classify input and analyze it by regression can be useful for many aspects of life. Around us, we can see the application of supervised learning. Examples of supervised learning are as follows:

1. Face recognition

In order for the system to recognize faces, supervised learning is used with a classification algorithm. Individuals can be identified based on image input, namely the individual’s face.

2. Determining property prices

The regression algorithm in supervised learning can be relied on to predict property prices. For example, if it’s a house, it means how many rooms, the size of the building and so on.

3. Predicting sales volume

The regression algorithm can be relied on to predict the volume of products to be sold. This prediction can analyze based on current market trends, historical data and other aspects.

4. Spam identification

Supervised learning can identify spam and open email messages using a classification algorithm. The identification of the email will use email metadata and text as considerations

5. Diagnosis

The use of classification algorithms can also be found in the medical field, namely when diagnosing diseases. The results are obtained based on symptom reports.

Stages of Supervised Learning

So what are the stages of implementing supervised learning? First of all, the dataset must be collected, this is a collection of paired and relevant input-outputs that have certain problems to be solved.

The data is prepared through normalization, division, handling missing values ​​and so on. It is also necessary to select a relevant algorithm based on the type of data and problem. The prepared training dataset will be used for model training according to the selected algorithm.

Furthermore, the evaluation dataset will be used to test the performance of the model. So, the model can be ensured not to be underfitting or overfitting. If it needs to be fixed, the model will be optimized using cross-validation and parameter tuning.

The trained model is used to predict new data. The model can then be applied to real practice.

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