| Algorithm |
Description |
When To Use |
Applications |
Advantages |
Disadvantages |
| Data Preprocessing |
Data preprocessing involves cleaning, transforming, and preparing raw data for machine learning algorithms. |
Before training any machine learning model to enhance its performance and accuracy. |
Data cleaning, feature scaling, handling missing values, encoding categorical variables. |
Improves the quality of data, reduces errors, enhances model performance. |
Time-consuming, requires domain knowledge, may lead to information loss. |
| Regression |
Regression is a statistical method used for predicting the value of a dependent variable based on one or more independent variables. |
When the target variable is continuous and you want to predict its value. |
Sales forecasting, stock price prediction, demand estimation. |
Provides insights into relationships between variables, easy to interpret. |
Assumes linear relationship, sensitive to outliers. |
| Simple Linear Regression |
Simple linear regression models the relationship between a single independent variable and a dependent variable using a linear function. |
When there is a linear relationship between two variables. |
Predicting house prices based on area, temperature, and time. |
Simple and easy to understand, provides a baseline model. |
Limited to linear relationships, may not capture complex patterns. |
| Multiple Linear Regression |
Multiple linear regression models the relationship between multiple independent variables and a dependent variable using a linear function. |
When there are multiple predictors influencing the target variable. |
Predicting house prices using features like area, number of bedrooms, and location. |
Incorporates multiple predictors, provides more accurate predictions. |
Assumes linear relationship, sensitive to multicollinearity. |
| Polynomial Regression |
Polynomial regression fits a polynomial curve to the data to capture non-linear relationships between variables. |
When the relationship between variables is non-linear. |
Modeling growth rates in biology, predicting stock prices with seasonal trends. |
Can model complex relationships, flexible. |
May overfit the data, requires careful selection of degree. |
| Support Vector Regression |
Support vector regression is a regression algorithm that uses support vector machines to find the best-fitting hyperplane while minimizing prediction errors. |
When dealing with small to medium-sized datasets with non-linear relationships. |
Stock price prediction, energy consumption forecasting. |
Effective in high-dimensional spaces, robust to overfitting. |
Can be computationally expensive, requires tuning of parameters. |
| Decision Tree Regression |
Decision tree regression builds a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. |
When the relationship between features and target variable is non-linear and the data is structured. |
Sales forecasting, predicting customer churn. |
Easy to understand and interpret, handles both numerical and categorical data. |
Prone to overfitting, sensitive to small variations in data. |
| Random Forest Regression |
Random forest regression is an ensemble learning method that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting. |
When dealing with complex non-linear relationships and large datasets. |
Predicting customer lifetime value, financial forecasting. |
Reduces overfitting, handles high-dimensional data, robust to noise and outliers. |
Less interpretable than individual decision trees, can be computationally expensive. |
| Classification |
Classification is a supervised learning task where the goal is to categorize input data into predefined classes or categories. |
When the target variable is categorical and you want to predict its class label. |
Email spam detection, sentiment analysis, image recognition. |
Provides clear insights into class boundaries, can handle both binary and multi-class problems. |
Requires labeled data for training, sensitive to imbalanced classes. |
| Logistic Regression |
Logistic regression is a statistical method used for binary classification problems. It models the probability that an instance belongs to a particular class. |
For binary classification problems. |
Credit risk analysis, medical diagnosis, customer churn prediction. |
Outputs have a probabilistic interpretation, simple and efficient for small datasets. |
Not suitable for complex relationships, requires feature engineering to avoid overfitting. |
| K-Nearest Neighbors (K-NN) |
K-Nearest Neighbors is a non-parametric lazy learning algorithm that classifies instances based on the majority class among their k-nearest neighbors in feature space. |
For classification and regression problems, especially when decision boundaries are not well-defined. |
Handwriting recognition, recommendation systems, anomaly detection. |
Simple and intuitive, no training phase, handles multi-class problems. |
Computationally expensive during testing, sensitive to irrelevant features and outliers. |
| Support Vector Machine (SVM) |
Support Vector Machine is a supervised learning algorithm used for classification and regression tasks. It finds the hyperplane that best separates classes in feature space. |
For binary classification problems, especially when dealing with high-dimensional data. |
Text classification, image recognition, bioinformatics. |
Effective in high-dimensional spaces, memory efficient, versatile due to kernel trick. |
Computationally expensive for large datasets, sensitive to noise and parameter tuning. |
| Kernel SVM |
Kernel SVM is an extension of SVM that allows for non-linear decision boundaries by transforming the feature space using kernel functions. |
When the data is not linearly separable. |
Image recognition, bioinformatics, text classification. |
Effective in high-dimensional spaces, handles non-linear relationships. |
Choosing the right kernel function and its parameters can be challenging. |
| Naive Bayes |
Naive Bayes is a probabilistic classifier based on Bayes' theorem with the assumption of independence between features. |
When dealing with text classification or when the independence assumption holds. |
Email spam filtering, document classification, sentiment analysis. |
Simple and efficient, works well with high-dimensional data, handles missing values. |
Assumes independence between features, can be outperformed by more complex models. |
| Decision Tree Classification |
Decision tree classification builds a model that predicts the class label of an instance by following a series of decision rules inferred from the data features. |
When the decision boundaries are non-linear and the data is structured. |
Customer segmentation, medical diagnosis, credit scoring. |
Easy to interpret, handles both numerical and categorical data. |
Prone to overfitting, sensitive to small variations in data. |
| Random Forest Classification |
Random forest classification is an ensemble learning method that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting. |
When dealing with complex non-linear relationships and large datasets. |
Image classification, fraud detection, recommendation systems. |
Reduces overfitting, handles high-dimensional data, robust to noise and outliers. |
Less interpretable than individual decision trees, can be computationally expensive. |
| Clustering |
Clustering is an unsupervised learning task where the goal is to group similar instances together into clusters. |
When there is no predefined label or target variable, and you want to explore the structure of the data. |
Customer segmentation, document clustering, anomaly detection. |
Reveals hidden patterns and structures in data, does not require labeled data. |
Choosing the right number of clusters can be subjective, sensitive to initialization. |
| K-Means Clustering |
K-Means clustering partitions the data into k clusters by iteratively assigning instances to the nearest cluster centroid and updating centroids. |
When the number of clusters is known or can be estimated, and clusters are spherical. |
Market segmentation, image compression, anomaly detection. |
Simple and computationally efficient, scales well to large datasets. |
Requires specifying the number of clusters in advance, sensitive to initial cluster centroids. |
| Hierarchical Clustering |
Hierarchical clustering builds a tree-like hierarchy of clusters by recursively merging or splitting clusters based on their similarity. |
When the number of clusters is not known or when the data has a hierarchical structure. |
Taxonomy creation, gene expression analysis, social network analysis. |
Does not require specifying the number of clusters in advance, captures hierarchical relationships. |
Computationally expensive, less scalable than K-Means. |
| Association Rule Learning |
Association rule learning discovers interesting relationships between variables in large datasets by identifying frequent itemsets and deriving association rules. |
When analyzing transactional data or market basket analysis. |
Market basket analysis, recommendation systems, cross-selling strategies. |
Reveals hidden patterns in data, interpretable rules. |
Scalability issues with large datasets, sensitive to noise and sparsity. |
| Apriori |
Apriori is a popular algorithm for mining frequent itemsets and generating association rules from transactional data. |
When analyzing transactional data to discover frequent itemsets and association rules. |
Market basket analysis, inventory management, website navigation analysis. |
Scalable to large datasets, easy to implement. |
Requires multiple passes over the data, computationally intensive for large itemsets. |
| Eclat |
Eclat is an alternative algorithm to Apriori for mining frequent itemsets from transactional data using a depth-first search approach. |
When scalability is a concern and dataset is sparse. |
Market basket analysis, web usage mining, customer segmentation. |
More memory efficient than Apriori, handles sparse datasets well. |
Limited to mining frequent itemsets, does not generate association rules directly. |
| Reinforcement Learning |
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trial and error, aiming to maximize cumulative rewards in a dynamic environment. |
When the environment is not fully known and the agent needs to learn through interaction. |
Game playing (e.g., AlphaGo), robotics, autonomous driving, recommendation systems. |
Can handle complex, dynamic environments; capable of learning optimal strategies without supervision. |
High computational requirements, exploration-exploitation trade-off, can be sensitive to hyperparameters. |
| Upper Confidence Bound (UCB) |
UCB is an algorithm used in multi-armed bandit problems where the goal is to balance exploration and exploitation. |
When dealing with decision-making under uncertainty with limited resources. |
Online advertising, clinical trials, resource allocation. |
Efficient exploration-exploitation trade-off, simple to implement. |
May not perform optimally in all scenarios, assumes stationarity. |
| Thompson Sampling |
Thompson Sampling is another approach to solve multi-armed bandit problems by using probability distributions over actions. |
Similar to UCB, used in problems where exploration-exploitation trade-off is crucial. |
Online advertising, clinical trials, resource allocation. |
Incorporates uncertainty naturally, can adapt to changing environments. |
Can be computationally intensive, requires prior knowledge. |
| Natural Language Processing |
NLP involves the interaction between computers and humans through natural language. |
When dealing with unstructured text data and tasks involving understanding, interpreting, and generating human language. |
Sentiment analysis, language translation, chatbots, information retrieval. |
Enables machines to understand and generate human language, facilitates communication between humans and machines. |
Ambiguity in language, domain-specific challenges, data scarcity for certain languages or tasks. |
| Deep Learning |
Deep Learning is a subset of machine learning where artificial neural networks with multiple layers learn representations of data. |
When dealing with large, complex datasets and tasks that involve pattern recognition. |
Image and speech recognition, natural language processing, autonomous driving. |
Capable of learning complex patterns, automatically extracts features, scalable with large datasets. |
Requires large amounts of data, computationally intensive, prone to overfitting. |
| Artificial Neural Networks (ANNs) |
ANNs are computing systems inspired by the biological neural networks of animal brains. |
When dealing with tasks like pattern recognition, classification, and regression. |
Image recognition, speech recognition, financial forecasting. |
Flexible architecture, capable of learning non-linear relationships. |
Prone to overfitting, black box nature, requires large amounts of data for training. |
| Convolutional Neural Networks (CNNs) |
CNNs are a type of deep neural network specifically designed for processing structured grid-like data. |
When dealing with tasks involving image recognition, computer vision, and spatial data. |
Object detection, image classification, medical image analysis. |
Hierarchical feature learning, parameter sharing, translation invariance. |
Requires large amounts of training data, computationally intensive. |
| Recurrent Neural Networks (RNNs) |
RNNs are neural networks designed to work with sequential data by maintaining a state or memory. |
When dealing with sequential data like time series, text, or speech. |
Language modeling, machine translation, speech recognition. |
Can handle variable-length sequences, captures temporal dependencies. |
Vulnerable to vanishing/exploding gradient problem, difficulty capturing long-term dependencies. |
| Self-Organizing Maps (SOMs) |
SOMs are a type of unsupervised learning neural network used for dimensionality reduction and visualization. |
When visualizing high-dimensional data or discovering patterns in data. |
Clustering, visualization of high-dimensional data. |
Topological ordering, dimensionality reduction, visual representation of data. |
Sensitivity to parameters, computationally expensive for large datasets. |
| Boltzmann Machines |
Boltzmann Machines are stochastic generative models that learn probability distributions over binary-valued data. |
When modeling complex data distributions or performing unsupervised learning tasks. |
Dimensionality reduction, feature learning, collaborative filtering. |
Capable of learning complex dependencies in data, unsupervised learning. |
Training can be slow, difficult to scale to large datasets. |
| AutoEncoders |
AutoEncoders are neural networks designed for unsupervised learning by learning to encode and decode data efficiently. |
When performing tasks like data denoising, dimensionality reduction, or feature learning. |
Anomaly detection, image denoising, recommendation systems. |
Can learn compact representations of data, unsupervised learning. |
Requires careful tuning of architecture and hyperparameters, sensitive to noise. |
| Dimensionality Reduction |
Dimensionality Reduction techniques aim to reduce the number of random variables under consideration by obtaining a set of principal variables. |
When dealing with high-dimensional data to simplify analysis and visualization. |
Visualization, noise reduction, feature extraction. |
Reduces computational complexity, removes redundant features, can improve model performance. |
May lose some information, requires careful selection of the number of dimensions. |
| Principal Component Analysis (PCA) |
PCA is a dimensionality reduction technique that identifies the directions (principal components) that maximize the variance in the data. |
When dealing with high-dimensional data to reduce its dimensionality while preserving most of its variance. |
Data visualization, noise reduction, feature extraction. |
Reduces dimensionality while preserving information, removes correlated features. |
Assumes linear relationships, may not perform well for non-linear data. |
| Linear Discriminant Analysis (LDA) |
LDA is a dimensionality reduction technique used in classification tasks to find the feature subspace that maximizes class separability. |
When performing classification tasks and reducing dimensionality. |
Pattern recognition, feature extraction, classification. |
Maximizes class separability, supervised dimensionality reduction. |
Assumes normal distribution of data, sensitive to outliers. |
| Kernel PCA |
Kernel PCA is a non-linear extension of PCA that uses kernel methods to project data into a higher-dimensional space before applying PCA. |
When dealing with non-linear data structures and traditional PCA is not sufficient. |
Non-linear dimensionality reduction, pattern recognition. |
Handles non-linear relationships, captures complex structures in data. |
Computational complexity increases with the size of the dataset, selection of appropriate kernel function is crucial. |
| Model Selection |
Model selection involves choosing the best model from a set of candidate models based on some evaluation criterion. |
When comparing multiple models to determine which one performs best for a given task. |
Machine learning, statistics, optimization. |
Improves generalization performance, selects the most suitable model for the problem. |
Requires careful evaluation metrics, can be computationally expensive. |
| k-Fold Cross Validation |
k-Fold Cross Validation is a technique used to estimate the performance of machine learning models by splitting the data into k subsets and training/testing the model k times. |
When evaluating the performance of a model and estimating its generalization error. |
Model evaluation, hyperparameter tuning. |
Provides more reliable performance estimates, reduces bias in performance evaluation. |
Can be computationally expensive, may introduce randomness. |
| Grid Search |
Grid Search is a technique used for hyperparameter optimization, where a grid of hyperparameter values is specified, and the best combination is selected based on model performance. |
When tuning hyperparameters of machine learning models. |
Hyperparameter optimization, model selection. |
Systematic approach to hyperparameter tuning, exhaustive search. |
Computationally expensive, may not scale well with high-dimensional hyperparameter spaces. |
| Boosting |
Boosting is an ensemble learning technique that combines multiple weak learners (simple models) sequentially to build a strong learner. |
Boosting is particularly useful when you have a large dataset and want to improve the performance of weak models. |
Classification and regression tasks in various domains such as finance, healthcare, marketing, and e-commerce. |
High predictive accuracy, robustness to overfitting, versatility, and handles imbalanced data well. |
Computationally expensive, sensitive to noisy data, requires careful parameter tuning, and less interpretable. |
| XGBoost |
XGBoost is an implementation of gradient boosting machines, a popular ensemble learning technique that builds a series of weak learners and combines them to make predictions. |
When dealing with structured/tabular data and aiming for high predictive accuracy. |
Regression, classification, ranking. |
High predictive accuracy, handles missing data, regularization. |
Requires careful tuning of hyperparameters, can be computationally expensive. |