In the past few years, based on my work experience and exchanges 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 the article published last year. 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 delve into the following six most important types of machine learning algorithms.
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Explanatory Algorithms
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Pattern Mining Algorithms
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Ensemble Algorithms
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Clustering Algorithms
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Time Series Algorithms
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Similarity Algorithms
Explanatory Algorithms
A major challenge in machine learning is understanding how various models arrive at their final predictions. We often know “what” the prediction is, but it is difficult to explain “why”.
Explanatory algorithms help us identify the variables that significantly impact the outcomes we care about. These algorithms enable us to understand the relationships between variables in the model, rather than just using the model to predict outcomes.
There are several algorithms that can be used to better understand the relationship 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—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 division.
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 interpreting machine learning model predictions, which builds a simpler model using techniques like linear regression or decision trees to locally approximate and explain predictions.
Shapley Additive Explanations (SHAP): An algorithm for interpreting machine learning model predictions by calculating the contribution of each feature to the prediction based on a method called “marginal contribution”. In some cases, it is more accurate than LIME.
Shapley Approximation (SHAP): A method for interpreting 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, which is generally faster than traditional 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 purchase 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 recurring 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 type of recurrent neural network designed to remember information for longer periods. LSTM can capture long-term dependencies in data, often used in tasks like 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 equivalently meaningful. This method can efficiently handle large datasets but may not be suitable for sparse data.
Prefix-projected Pattern Mining (PrefixSpan): An algorithm that finds 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 predictions that are more accurate than any single model. There are several reasons why ensemble algorithms can outperform traditional machine learning algorithms:
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Diversity. By combining predictions from multiple models, ensemble algorithms can capture a wider range of patterns in the data.
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Robustness. Ensemble algorithms are typically less sensitive to noise and outliers in the data, which can make predictions more stable and reliable.
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Reduction of overfitting. By averaging predictions from multiple models, ensemble algorithms can reduce overfitting of individual models to the training data, thus improving generalization to new data.
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Improved accuracy. Ensemble algorithms have been shown to maintain an advantage over traditional machine learning algorithms in various scenarios.
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, clustering algorithms do not have a target variable.
This technique is very useful for finding natural patterns and trends in data and is often used during the data analysis phase to gain further insights into the data. Additionally, clustering algorithms can be used to segment datasets into different parts based on various variables, a common application being in 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 easy to implement.
DBSCAN Density Clustering: A density-based clustering algorithm that can identify clusters of arbitrary shapes. 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 group data points into clusters, capable of handling non-linearly separable data and relatively efficient.
Time Series Algorithms
Time series algorithms are techniques used to analyze data that is related to time. These algorithms take into account the temporal dependencies between data points in a series, which is especially important for 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, designed to be intuitive and easy to use. Some of its key advantages include handling missing data and forecasting trend changes, with robustness to outliers and fast fitting.
AutoRegressive Integrated Moving Average (ARIMA): A statistical method for forecasting time series data, modeling 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 a weighted average of past data to make 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 (such as Euclidean distance) or the similarity of texts (such as the Levenshtein algorithm).
These algorithms have wide applications, especially in recommendations. They can be used to identify similar items or recommend related content to users.
Algorithms
Euclidean Distance: A measure 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 unevenly distributed data.
Cosine Similarity: A measure of similarity based on the angle between two vectors.
Levenshtein Algorithm: An algorithm for measuring the distance between two strings, based on the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. The Levenshtein algorithm is often used in spelling correction and string matching tasks.
Jaro-Winkler Algorithm: An algorithm for measuring 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 decomposes a matrix into the product of three matrices, and is an important component of state-of-the-art recommendation systems.