What Is Supervised Learning?

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Supervised learning is a machine learning approach defined by its use of labeled datasets to train algorithms into producing a desired outcome or result. Its goal is to learn a function that accurately determines the recognizable relationship between the input and output data, given a sample of labeled datasets.

In supervised learning models, the algorithm adjusts the weights of input data while being repeatedly trained until its predictions approximate the desired output.

What Are the Problems It Can Solve?

Supervised learning isn’t suitable for all types of problems—it’s the best approach only for the following:

Classification: A classification problem is when a model needs to sort items into different categories. The algorithm recognizes specific entities in the dataset based on their features and infers how they should be defined. For example, you want to build a model segregating cats from dogs, or identifying different types of structures in a neighborhood. 

Regression: If you want to establish the relationship between two variables, you’ll use regression algorithms to predict an outcome. Commonly used regression algorithms include linear regression and logistic regression. This model is often applied to solve problems, such as projecting sales revenues or predicting stock price movement.

The Challenges of Supervised Learning 

Many of the challenges inherent to supervised learning lie in the human supervisors. Curating accurately labeled datasets from teams of humans is a challenge that spans technology, communication, and consistency. In our recent webinar with Label Studio, you can see how data labeling can be user-friendly and version controlled with Pachyderm. 

Why version control your labeled datasets? Because accurate labeling can be very hard: confusing instructions, technology problems, and other challenges can compromise the integrity of your data labeling. This blog post dives deeper into the reasons that a data labeling project needs version control

Applications of Supervised Learning

Below are some use cases of supervised learning models:

Spam Detection: Developers can use classification algorithms to identify patterns within incoming correspondences and effectively separate spam from non-spam emails.

Image and Object Recognition: Computer vision and image analysis utilize supervised learning algorithms to recognize, isolate, and classify objects in videos or images. This technology is often seen on face-unlock features of phones and recommendation engines.

Strategic Business Decisions: Regression algorithms are widely used for developing predictive models based on dependent and independent data. Businesses can implement and justify strategies according to forecasted or expected results.

 

Supervised Learning and Pachyderm

If the best approach to your machine learning project is supervised learning, you’ll need a good platform for managing your data. Pachyderm offers the best-in-class data versioning, pipelines, and lineage so you can scale projects with ease. Try Pachyderm’s integration with Label Studio to see how simple it can be to add version control to your data labeling.  

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