A Comprehensive Guide to Machine Learning Algorithms

In the past few years, based on my work experience and communication with other data scientists, including content read online, I have compiled what I consider to be the most important machine learning algorithms.

This year, I want to provide more models across categories based on last year’s article. I hope to offer a treasure trove of tools and techniques that you can bookmark to solve various data science problems.

With that said, let’s dive into the following six most important types of machine learning algorithms.

Explanatory Algorithms
Pattern Mining Algorithms
Ensemble Algorithms
Clustering Algorithms
Time Series Algorithms
Similarity Algorithms

Explanatory Algorithms

A major challenge in machine learning is understanding how various models arrive at their final predictions; we often know the “what,” but find it hard to explain the “why.”

Explanatory algorithms help us identify variables that significantly impact the outcomes of interest. These algorithms enable us to understand the relationships between variables in the model, rather than just using the model to predict outcomes.

Several algorithms can be used to better understand the relationships between independent and dependent variables of a model.

Algorithms

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 relationship between variables based on t-tests and coefficients.

Decision Trees: A machine learning algorithm that creates a tree-like model of decisions and their possible consequences, helping to understand the relationships between variables by observing the rules of branching.

Principal Component Analysis (PCA): A dimensionality reduction technique that projects data into a lower-dimensional space while retaining as much variance as possible. PCA can be used to simplify data or identify important features.

Local Interpretable Model-agnostic Explanations (LIME): An algorithm for explaining machine learning model predictions that builds a simpler model using techniques like linear regression or decision trees to locally approximate the model.

Shapley Additive Explanations (SHAP): An algorithm for explaining machine learning model predictions that calculates each feature’s contribution to the prediction based on the “marginal contribution” method. In some cases, it is more accurate than SHAP.

Shapley Approximation (SHAP): A method for explaining machine learning model predictions by estimating the importance of each feature in the prediction. SHAP uses a method called “cooperative game” to approximate Shapley values, typically faster than SHAP.

Pattern Mining Algorithms

Pattern mining algorithms are a data mining technique used to identify patterns and relationships in datasets. These algorithms can be used for various purposes, such as identifying customer purchasing patterns in retail, understanding common user behavior sequences on websites/apps, or finding relationships between different variables in scientific research.

Pattern mining algorithms typically work by analyzing large datasets and looking for repeated patterns or associations between variables. Once these patterns are identified, they can be used to predict future trends or outcomes or to understand underlying relationships in the data.

Algorithms

Apriori Algorithm: An algorithm used to find frequent itemsets in transaction databases—efficient and widely used for association rule mining tasks.

Recurrent Neural Networks (RNN): A neural network algorithm designed to handle sequential data, capable of capturing temporal dependencies in the data.

Long Short-Term Memory Networks (LSTM): A recurrent neural network designed to remember information for longer periods. LSTMs can capture long-term dependencies in the data and are commonly used in tasks such as language translation and generation.

Sequential Pattern Discovery using Equivalence Classes (SPADE): A method for finding frequently occurring patterns in sequential data by grouping items that are equivalent in some sense. This method can efficiently handle large datasets but may not be suitable for sparse data.

PrefixSpan Pattern Mining: An algorithm for finding common patterns in sequential data by constructing a prefix tree and pruning infrequent items. PrefixSpan can efficiently handle large datasets but may not be suitable for sparse data.

Ensemble Algorithms

As a machine learning technique, ensemble algorithms combine multiple models to make more accurate predictions than any individual model. There are several reasons why ensemble algorithms can outperform traditional machine learning algorithms:

Diversity. By combining predictions from multiple models, ensemble algorithms can capture a broader range of patterns in the data.
Robustness. Ensemble algorithms are typically less sensitive to noise and outliers in the data, making predictions more stable and reliable.
Reduction of Overfitting. By averaging predictions from multiple models, ensemble algorithms can reduce overfitting of individual models to training data, improving generalization to new data.
Increased Accuracy. Ensemble algorithms have been shown to maintain advantages over traditional machine learning algorithms in various situations.

Algorithms

Random Forest: A machine learning algorithm that builds a collection of decision trees and makes predictions based on the majority “vote” of the trees.

XGBoost: A gradient boosting algorithm that uses decision trees as its base model, known as one of the strongest machine learning prediction algorithms.

LightGBM: Another gradient boosting algorithm designed to be faster and more efficient than other boosting algorithms.

CatBoost: A gradient boosting algorithm specifically designed to handle categorical variables.

Clustering Algorithms

Clustering algorithms are a type of unsupervised learning task used to group data into “clusters.” Unlike supervised learning, where the target variable is known, there is no target variable in clustering algorithms.

This technique is very useful for finding natural patterns and trends in data and is often used in the data analysis phase to gain further insight into the data. Additionally, clustering algorithms can be used to segment datasets into different parts based on various variables, a common application being customer or user segmentation.

Algorithms

K-Modes Clustering: A clustering algorithm specifically designed for categorical data, capable of handling high-dimensional categorical data well, and relatively simple to implement.

DBSCAN Density Clustering: A density-based clustering algorithm that can identify clusters of arbitrary shape. It is relatively robust to noise and can identify outliers in the data.

Hierarchical Clustering: A clustering algorithm that uses the eigenvectors of a similarity matrix to assign data points to clusters, capable of handling non-linearly separable data and is relatively efficient.

Time Series Algorithms

Time series algorithms are techniques used to analyze data related to time. These algorithms consider the temporal dependencies between data points in a series, which is particularly important when predicting future values.

Time series algorithms are used in various business applications, such as forecasting product demand, sales, or analyzing customer behavior over time; they can also be used to detect anomalies or trend changes in the data.

Algorithms

Prophet Time Series Model: A time series forecasting algorithm developed by Facebook that is intuitive and easy to use. Some of its main advantages include handling missing data and predicting trend changes, being robust to outliers, and fitting quickly.

Autoregressive Integrated Moving Average (ARIMA): A statistical method for forecasting time series data that models the correlation between the data and its lagged values. ARIMA can handle a wide range of time series data but is more difficult to implement than some other methods.

Exponential Smoothing: A method for forecasting time series data that uses weighted averages of past data for predictions. Exponential smoothing is relatively simple to implement and can be used for a wide range of data but may not perform as well as more complex methods.

Similarity Algorithms

Similarity algorithms are used to measure the similarity between a pair of records, nodes, data points, or texts. These algorithms can be based on the distance between two data points (e.g., Euclidean distance) or the similarity of texts (e.g., Levenshtein algorithm).

These algorithms have a wide range of applications, particularly in recommendations. They can be used to identify similar items or recommend relevant content to users.

Algorithms

Euclidean Distance: A measurement of the straight-line distance between two points in Euclidean space. Euclidean distance is simple to compute and widely used in machine learning, but may not be the best choice in cases of uneven data distribution.

Cosine Similarity: Measures the similarity between two vectors based on the angle between them.

Levenshtein Algorithm: An algorithm that measures the distance between two strings based on the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into the other. The Levenshtein algorithm is commonly used in spelling correction and string matching tasks.

Jaro-Winkler Algorithm: An algorithm that measures the similarity between two strings based on the number of matching characters and the number of transpositions. It is similar to the Levenshtein algorithm and is often used in record linkage and entity resolution tasks.

Singular Value Decomposition (SVD): A matrix decomposition method that breaks a matrix into the product of three matrices, which is an important component in state-of-the-art recommendation systems.

This article is sourced from: Mathematics China