Tree-Based
Random Forest
Ensemble learning for robust classification and regression.
RF
Use case
Predicting loan default risk in banking.
Showing 20 algorithms
Tree-Based
Linear & Regression
Clustering & Similarity
Probabilistic / Margin
Dimensionality Reduction
Linear & Regression
Logistic Regression
A baseline classifier for binary and multiclass prediction tasks.
LR
Use case
Classifying patients as high-risk for disease.
Tree-Based
Gradient Boosting (GBM)
Sequential boosting that improves performance by correcting prior errors.
GBM
Use case
Detecting fraudulent transactions in e-commerce.
Probabilistic / Margin
Support Vector Machines (SVM)
A margin-based method effective for structured classification problems.
SVM
Use case
Classifying handwritten digits in image datasets.
Clustering & Similarity
K-Nearest Neighbors (KNN)
A similarity-based method that predicts outcomes from nearby examples.
KNN
Use case
Recommending movies based on user preferences.
Tree-Based
XGBoost
A highly optimized boosting library known for speed and predictive power.
XGB
Use case
Winning machine learning competitions with faster, accurate predictions.
Tree-Based
Decision Trees
Interpretable models that split decisions into easy-to-follow rules.
DT
Use case
Determining customer eligibility for credit cards.
Probabilistic / Margin
Naive Bayes
A lightweight probabilistic model that works well on text-heavy problems.
NB
Use case
Email spam classification based on keyword probabilities.
Clustering & Similarity
K-Means Clustering
An unsupervised technique for partitioning data into natural groups.
KM
Use case
Grouping online shoppers by behavior patterns.
Linear & Regression
Linear Regression
A core regression method for understanding and forecasting numeric outcomes.
LIN
Use case
Forecasting monthly sales in retail.
Tree-Based
AdaBoost
A boosting approach that combines weak learners into a stronger predictor.
ADA
Use case
Enhancing performance in face recognition tasks.
Tree-Based
LightGBM
A fast gradient boosting framework designed for scale and efficiency.
LGB
Use case
Accelerating model training on large datasets.
Tree-Based
CatBoost
Boosting optimized for tabular data with strong handling of categorical variables.
CAT
Use case
Handling categorical features in business applications.
Linear & Regression
Ridge Regression
Regularized regression that improves stability when predictors overlap.
RR
Use case
Handling collinear variables in housing price models.
Linear & Regression
Lasso Regression
Regularized regression that helps select the most important predictors.
LAS
Use case
Selecting most relevant predictors in marketing data.
Clustering & Similarity
DBSCAN (Clustering)
Density-based clustering that handles noise and irregular cluster shapes.
DB
Use case
Detecting anomalies in network traffic.
Dimensionality Reduction
PCA (Principal Component Analysis)
A dimensionality reduction technique for compressing and simplifying feature space.
PCA
Use case
Reducing feature space for image compression.
Clustering & Similarity
Hierarchical Clustering
A clustering method that reveals nested relationships between groups.
HC
Use case
Building family trees of similar customer behaviors.
Linear & Regression
Polynomial Regression
A regression approach for modeling curved relationships in data.
PR
Use case
Modeling growth curves in scientific experiments.
Linear & Regression
Stochastic Gradient Descent (SGD)
An optimization workhorse for training models efficiently at scale.
SGD
Use case
Training large-scale linear classifiers efficiently.