Machine learning models are computer programs that are used to recognize patterns in data or make predictions.

Machine learning models are created from machine learning algorithms, which undergo a training process using either labeled, unlabeled, or mixed data. Different machine learning algorithms are suited to different goals, such as classification or prediction modeling, so data scientists use different algorithms as the basis for different models. As data is introduced to a specific algorithm, it is modified to better manage a specific task and becomes a machine learning model.

How to create a machine learning model

Machine learning models are created by training algorithms with either labeled data, unlabeled data, or a mix of both. Four primary machine learning algorithms exist:

1. Supervised learning:

Supervised learning occurs when an algorithm is trained using “labeled data,” or data that is tagged with a label so that an algorithm can successfully learn from it. Training labels help the eventual machine learning model know how to classify data in the manner that the researcher desires.

2. Unsupervised learning:

Unsupervised algorithms use unlabeled data to train an algorithm. In this process, the algorithm finds patterns in the data itself and creates its own data clusters. Unsupervised learning and pattern recognition are helpful for researchers who are looking to find patterns in data that are currently unknown to them.

3. Semi-supervised learning:

Semi-supervised learning uses a mix of labeled and unlabeled data to train an algorithm. In this process, the algorithm is first trained with a small amount of labeled data before being trained with a much larger amount of unlabeled data.

4. Reinforcement learning:

 Reinforcement learning is a machine learning technique in which positive and negative values are assigned to desired and undesired actions. The goal is to encourage programs to avoid the negative training examples and seek out the positive, learning how to maximize rewards through trial and error. Reinforcement learning can be used to direct unsupervised machine learning.

Types of machine learning models

There are two types of problems that dominate machine learning: classification and prediction.

Classification models

• Logistic regression
• Naive Bayes
• Decision trees
• Random forest
• K-nearest neighbor (KNN)
• Support vector machine

Regression models

• Linear regression
• Ridge regression
• Decision trees
• Random forest
• K-nearest neighbor (KNN)
• Neural network regression