All Machine Learning Algorithms You Should Know for 2023
For the past few years, I’ve compiled what I believed were the most important machine learning algorithms based on my experiences at work, my conversations with other data scientists, and what I read online.
This year, I want to expand on last year’s article by providing more types of models, as well as more models within each category. Through this, I hope to provide a repository of tools and techniques that you can bookmark so that you can tackle a variety of data science problems!
With that said, let’s dive into six of the most important types of machine learning algorithms:
- Explanatory algorithms
- Pattern mining algorithms
- Ensemble learning algorithms
- Clustering algorithms
- Time series algorithms
- Similarity algorithms
1. Explanatory Algorithms
One of the biggest problems in machine learning is understanding how various models get to their end predictions. We often know the “what” but struggle to explain the “why”.
Explanatory algorithms help us identify the variables that have a meaningful impact on the outcome we are interested in. These algorithms allow us to understand the relationships between the variables in the model, rather than just using the model to make predictions about the outcome.
There are several algorithms that you can use to better understand the relationships between the independent variables and the dependent variable for a given model.
- Linear/Logistic Regression: a statistical method for modeling the linear relationship between a dependent variable and one or more independent variables — can be used to understand the relationships between variables based on the t-tests and coefficients.
- Decision Trees: a type of…