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Machine Learning Algorithms Cheat Sheet

Here’s a comprehensive cheat sheet for some commonly used machine learning algorithms, categorized by type and use case. This guide briefly describes key points and typical applications for each algorithm.

An-Overview-of-Key-Machine-Learning-Algorithms-(1)

Machine Learning Algorithms Cheat Sheet

This article provides an overview of key algorithms in each category, their purposes, and best use-cases.

Types of Machine Learning Algorithms

Normally the Machine Learning Algo can be divided into 4 types:

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning
  4. Ensemble Learning

Table of Content

Supervised Learning Algorithms

Supervised learning involves training a model on a labeled dataset, meaning that each training example is paired with an output label. The goal is to learn a mapping from inputs to outputs that can be used to predict labels for new, unseen data. Common Supervised Learning Algorithms includes:

  1. Linear Regression: Linear regression is used for predicting a continuous output variable based on one or more input features. It assumes a linear relationship between the input variables and the output variable.
  2. Logistic Regression: Logistic regression is used for binary classification problems. It predicts the probability that a given input belongs to a certain class.
  3. Decision Trees: Decision trees are used for both classification and regression tasks. They split the data into subsets based on the value of input features, creating a tree-like model of decisions.
  4. Random Forest: Random forest is an ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees.
  5. Support Vector Machines (SVM): SVMs are used for classification and regression tasks. They work by finding the hyperplane that best separates the classes in the feature space.
  6. k-Nearest Neighbors (k-NN): k-NN is a simple, instance-based learning algorithm used for classification and regression. It predicts the label of a new instance based on the majority label of its k nearest neighbors in the training set.
  7. Naive Bayes: Naive Bayes classifiers are based on Bayes’ theorem and assume independence between features. They are particularly effective for text classification problems.
Supervised Learning Algorithms Description Purpose Best Use-Cases
Linear Regression Predicts continuous output based on input features. Predict continuous numerical outcomes. Predicting house prices, Forecasting sales or revenue.
Logistic Regression Predicts probability of belonging to a certain class. Classify data into two distinct classes. Spam detection, Predicting customer purchases.
Decision Trees Splits data into subsets based on input features. Simplify decision-making processes. Customer segmentation, Diagnosing diseases.
Random Forest Ensemble learning method combining multiple decision trees. Improve prediction accuracy and control overfitting. Credit scoring, Predicting stock prices.
Support Vector Machines (SVM) Finds the hyperplane that best separates classes. Maximize the margin between classes. Image classification, Handwriting recognition.
k-Nearest Neighbors (k-NN) Predicts based on proximity to known data points. Classify or predict based on proximity to known data points. Recommender systems, Intrusion detection.
Naive Bayes Classifies based on probabilistic relationships. Classify data based on probabilistic relationships. Spam filtering, Sentiment analysis.

Unsupervised Learning Algorithms

Unsupervised learning deals with unlabeled data. The goal is to find hidden patterns or intrinsic structures in the input data. Unsupervised Learning Algorithms include:

  1. k-Means Clustering: k-Means is a clustering algorithm that partitions the data into k clusters, where each data point belongs to the cluster with the nearest mean.
  2. Hierarchical Clustering: Hierarchical clustering builds a hierarchy of clusters either by a bottom-up approach (agglomerative) or a top-down approach (divisive).
  3. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms the data into a new coordinate system, reducing the number of dimensions while preserving as much variance as possible.
  4. t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is a non-linear dimensionality reduction technique particularly well-suited for visualizing high-dimensional datasets.
  5. Apriori Algorithm: The Apriori algorithm is used for mining frequent itemsets and learning association rules. It is commonly used in market basket analysis.
Unsupervised Learning Algorithms Description Purpose Best Use-Cases
k-Means Clustering Partitions data into k clusters based on nearest mean. Group similar data points together. Market segmentation, Document clustering.
Hierarchical Clustering Builds a hierarchy of clusters using agglomerative or divisive approaches. Create a hierarchy of nested clusters. Gene expression data analysis, Social network analysis.
Principal Component Analysis (PCA) Reduces dimensionality by transforming data into a new coordinate system. Reduce dimensionality of data. Image compression, Feature extraction.
t-Distributed Stochastic Neighbor Embedding (t-SNE) Non-linear dimensionality reduction for visualizing high-dimensional datasets. Visualize high-dimensional data. Visualizing clusters in data, Exploring patterns in large datasets.
Apriori Algorithm Mines frequent itemsets and learns association rules. Discover association rules in large datasets. Market basket analysis, Recommender systems.

Reinforcement Learning Algorithms

Reinforcement learning (RL) involves training an agent to make a sequence of decisions by rewarding it for good actions and penalizing it for bad ones. Common Reinforcement Learning Algorithms includes:

  1. Q-Learning: Q-Learning is a model-free RL algorithm that seeks to learn the value of an action in a particular state. It uses a Q-table to store the value of state-action pairs.
  2. Deep Q-Networks (DQN): DQN combines Q-learning with deep neural networks to handle high-dimensional state spaces. It uses experience replay and target networks to stabilize training.
  3. Policy Gradient Methods: Policy gradient methods optimize the policy directly by maximizing the expected reward. They are particularly useful for environments with continuous action spaces.
  4. Actor-Critic Methods: Actor-critic methods combine value-based and policy-based approaches. The actor updates the policy, while the critic evaluates the action taken by the actor.
Reinforcement Learning Algorithms Description Purpose Best Use-Cases
Q-Learning Model-free RL algorithm that learns the value of an action in a particular state using a Q-table. Learn optimal actions in a given environment. Game playing, Robotics.
Deep Q-Networks (DQN) Combines Q-learning with deep neural networks to handle high-dimensional state spaces. Handle complex state spaces. Autonomous driving, Complex strategy games.
Policy Gradient Methods Optimize the policy directly by maximizing the expected reward. Optimize policies in continuous spaces. Robotics control, Financial trading.
Actor-Critic Methods Combine value-based and policy-based approaches to balance exploration and exploitation. Balance exploration and exploitation. Real-time strategy games, Dynamic resource allocation.

Ensemble Learning Algorithms

Ensemble Learning Algorithm combine multiple machine learning techniques to improve performance. Popular Ensemble Learning Algorithms include:

  • Gradient Boosting Machines (GBM): GBM is an ensemble technique that builds models sequentially, each new model correcting errors made by the previous ones.
  • XGBoost: XGBoost is an optimized implementation of gradient boosting that is efficient and scalable. It is widely used in machine learning competitions.
  • LightGBM: LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be distributed and efficient, making it suitable for large datasets.
Ensemble Learning Algorithms Description Purpose Best Use-Cases
Gradient Boosting Machines (GBM) Ensemble technique that builds models sequentially, each new model correcting errors made by the previous ones. Improve model accuracy through boosting. Predictive modeling, Fraud detection.
XGBoost Optimized implementation of gradient boosting that is efficient and scalable. Provide efficient and scalable boosting. Kaggle competitions, Large-scale data analysis.
LightGBM Gradient boosting framework that uses tree-based learning algorithms, designed to be distributed and efficient. Efficiently handle large datasets. Large-scale prediction tasks, High-dimensional data analysis.

Conclusion

Machine learning algorithms are powerful tools for extracting insights from data and making predictions. Understanding the key algorithms in supervised, unsupervised, and reinforcement learning can help you choose the right approach for your specific problem. Whether you’re dealing with labeled data, unlabeled data, or dynamic environments, there’s a machine learning algorithm that can help you achieve your goals.


 


Reffered: https://www.geeksforgeeks.org

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