In supervised learning, engineers use labeled data sets in order to train algorithms. By labeling outputs and matching inputs to corresponding outputs fed into the algorithm, machine learning models are able to weigh accuracy and improve with additional data repetition over time.
What Are the Types of Supervised Learning?
Supervised learning can be completed through the use of algorithms like naive Bayes and decision trees, or tasks such as regression and classification. The use of various algorithms determine the types of supervised learning and the tasks that supervised learning is capable of completing.
Types of Supervised Learning
- Regression algorithm produce a single, probabilistic output value that is determined based on the degree of correlation between the input variables.
- Classification algorithms separate and group data into different classes.
- Naive Bayesian models are a type of classification for large finite data sets into parent nodes and independent children nodes.
- Decision trees contain conditional control statements that include decisions and probable consequences in order to produce outputs that label unforeseen data.
- Random forest models contain multitudes of decision trees and output classifications of individual trees.
- Neural networks are designed to cluster raw inputs, recognize patterns or interpret sensory data.
What Is the Difference Between Supervised and Unsupervised Learning
The biggest difference between supervised and unsupervised learning is the use of labeled data sets.
Supervised learning is the act of training the data set to learn by making iterative predictions based on the data while adjusting itself to produce the correct outputs. By providing labeled data sets, the model already knows the answer it is trying to predict but doesn’t adjust the process until it produces an independent output.
The difference between the prediction and the correct answer is the key to the models producing accurate predictions when using new data. Like most varieties of machine learning, supervised learning is typically used to predict outcomes from data. It is calculated through Python or R and can be time-consuming to train.
Unsupervised learning does not make use of labeled data sets, meaning the models work on their own to uncover the inherent structure of the unlabeled data. Human intervention is still required to validate the output variables depending on the intended use of the data and if it makes sense for the data to be utilized.
Unsupervised learning is typically used to uncover insights from massive volumes of data, detect anomalies or make recommendations and may have inaccurate results without human validation.
What Is an Example of Supervised Learning?
Supervised learning can be used to make accurate predictions using data, such as predicting a new home’s price.
In order for predictions to be made, input data must be gathered. To determine a new home’s price, for example, we need to know factors like location, square footage, outdoor space, number of floors, number of rooms and more. The home’s price represents the output, or label, while the factors like location, square footage and outdoor space represent the input data for the algorithm.
Once the corresponding labels are set, data from thousands of other homes can be gathered and compared against the existing input data. By weighing the features against the prices of these other homes, the model can do the same for the home in question to determine an accurate price.