Supervised vs semi-supervised vs unsupervised machine learning
There are three machine learning types to remember: supervised, semi-supervised, and unsupervised machine learning. As we’ve discussed, machine learning relies on a human inputting data to train it how to behave. Supervised learning fully labels and tags the data with the answer.
Unfortunately, fully labeled data isn’t always possible. Unsupervised learning is a learning model with a dataset that comes without instructions. This dataset is a collection of examples without a desired output or outcome. Ultimately, there is no correct answer fed to the machine.
Instead, the machine must find a way to structure the data and extract useful features by analyzing it. The unsupervised machine learning model organizes data in a few separate ways, including:
- Clustering: Clustering allows machines to draw conclusions based on their given data. By closing groups of training data together, they can draw some conclusions.
- Anomaly detection: Anomaly detection can sort through data and find instances of a break in the pattern.
- Association: Association allows machines to determine which new data should be grouped together based on training data.
What makes supervised and unsupervised learning different is that unsupervised learning is less accurate because it hasn’t been trained with the right answers.
The final type of machine learning is semi-supervised learning, which is a happy medium between accurate supervised learning and less accurate unsupervised learning. Semi-supervised learning requires a training dataset with labeled and unlabeled data. Then, it learns what it can from the labeled data and draws conclusions from the unlabeled data.
Benefits of supervised learning models
Machine learning allows you to accomplish more because a machine can analyze large sets of data for you. There are several benefits of supervised machine learning for your business, including the following:
Improved decision making
Supervised machine learning is accurate because it already knows the answers thanks to its training data. Since it’s primarily rules-based, it only matches records that fit the right conditions.
For example, you can use it to segment customers based on age and know that it’s correct because you’ve already told it what to do and how to do it. Supervised machine learning can be used to make more accurate financial predictions because it consumes unlimited data that it can sort through much faster than a human, allowing you to make better decisions based on error-free data.
Better customer insights
Supervised machine learning can improve your customer insights to help you learn more about customer behavior. Machine learning allows you to analyze information you’ve collected on customers, including recent behaviors and purchases, and interpret them.
Sifting through data is time-consuming, and humans easily make mistakes. Supervised machine learning simplifies data entry to mitigate risks associated with accounting or bookkeeping errors. It can also help you calculate customer lifetime values.
For example, you can use data to learn about customer behaviors and predict the probability of conversions.
Challenges with machine learning
Unfortunately, supervised machine learning isn’t perfect. There are several challenges, including the following:
There are many types of bias in statistics that can be built into machine learning and AI. Since supervised learning depends on a dataset for answers, it’s easy to build bias into it without realizing it.
Machine learning bias, or AI bias, occurs within supervised learning models because of assumptions they’ve made while learning.
Unfortunately, this bias can cause imbalances in data and issues evaluating the data to provide accurate predictions. Additionally, humans evaluate the outcomes and can create their own biases when reading data compiled by machine learning.
Poor quality data
Data plays a significant role in how supervised learning models behave. If you feed them poor-quality data, they’ll have a poor-quality output.
For machines to learn, there has to be enough data. Machine learning is sophisticated, but it’s not as sophisticated as the human brain (yet). Therefore, it requires tons of data to learn, including thousands of examples and answers.
Not having enough data means getting inaccurate results because the computer relies on examples and must be fed the answer to get it right every time.
Machine learning is expensive, and it can be challenging to find a data engineer. Supervised machines rely on millions of examples, which is time-consuming and expensive.