Machine Learning
75 terms in the Machine Learning domain — each bilingual TR/EN with related-term graph.
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All Terms (75)
ARIMA
A classical model for time series forecasting that combines autoregressive and moving-average components.
AdaBoost
A boosting method that turns weak learners into a strong ensemble by focusing increasingly on hard examples.
Affinity Propagation
A clustering algorithm that forms clusters through message passing around representative exemplars.
Autoencoder-Based Anomaly Detection
A deep learning approach that learns normal patterns and detects anomalies through reconstruction error.
Autoencoder-Based Dimensionality Reduction
An approach that learns lower-dimensional representations of data through neural-network-based compression.
BIRCH Clustering
A tree-based method suited to incremental and memory-efficient clustering on large datasets.
Bagging
An ensemble approach that improves stability by training multiple models on bootstrap samples and combining their outputs.
Bayesian Linear Regression
A linear regression approach that treats model parameters as probability distributions rather than fixed values.
Bayesian Network
A probabilistic model that represents conditional dependencies among variables through a directed graph structure.
Bayesian Optimization
A sample-efficient optimization method that intelligently selects new hyperparameter candidates by learning from past trials.
Calibrated Classification
An approach that aims to make a classifier’s probability outputs more consistent with true observed frequencies.
CatBoost
An advanced ensemble method that combines boosting with strong native handling of categorical variables.
Collaborative Filtering
A core recommendation approach that generates suggestions through similar users or similar items.
Conditional Random Field
A conditional probabilistic graphical model used especially in sequential labeling problems.
Consensus Clustering
An approach that combines multiple clustering results to obtain a more stable and reliable cluster structure.
Content-Based Filtering
A recommendation method that relies on item features and a user's historical preference profile.
Cost-Sensitive Classification
An approach that incorporates different misclassification costs into the decision process instead of treating all errors equally.
Elastic Net Regression
A regression method that combines L1 and L2 regularization to provide both coefficient shrinkage and partial feature selection.
Elliptic Envelope
A statistical anomaly method that detects outliers by assuming the data follows an approximately elliptical distribution.
Exponential Smoothing
A time series method that assigns weighted importance to past observations and can model level, trend, and seasonality.
Gaussian Mixture Model
A probabilistic model that assumes the data is generated from a mixture of multiple Gaussian distributions.
Gradient Boosting
A boosting-based ensemble method that builds strong predictions by sequentially reducing the errors of previous models.
Grid Search
A method that searches for the best model by systematically trying predefined hyperparameter combinations.
HDBSCAN
An advanced clustering method that detects clusters with varying density levels through a hierarchical density-based approach.
Hidden Markov Model
A sequential probabilistic structure that models hidden state transitions behind observable outputs.
Hierarchical Clustering
A clustering approach that represents similarity among data points through a tree-like hierarchical structure.
Huber Regression
A robust regression method that is more resistant to outliers than ordinary least squares.
Hyperband
An optimization method that uses dynamic resource allocation to eliminate poor hyperparameter candidates early.
Implicit Feedback Recommendation
A recommendation approach that relies on behavioral signals such as clicks, views, and purchases instead of explicit ratings.
Independent Component Analysis
A dimensionality reduction and separation method that aims to decompose mixed signals into statistically independent components.
Isolation Forest
An anomaly detection method based on the assumption that anomalous instances are easier to isolate.
Isomap
A nonlinear dimensionality reduction method that seeks to preserve manifold structure through approximate geodesic distances.
Lasso Regression
An L1-based regression method that can perform both regularization and feature selection by driving coefficients to zero.
LightGBM
A tree-based method that delivers fast and efficient boosting performance on large-scale tabular problems.
Linear Discriminant Analysis
A statistical classification method that seeks linear projections that best separate classes.
Linear Regression
A fundamental regression algorithm that models the linear relationship between input variables and a target variable.
Local Outlier Factor
A method that measures outlierness by comparing a point's local density to that of its neighbors.
Logistic Regression
A foundational classification algorithm that uses the logit function to model class probabilities.
Matrix Factorization
A powerful recommendation approach that generates suggestions by decomposing the user-item interaction matrix into latent factors.
Mean Shift
A clustering method that discovers clusters by seeking density modes and does not require the number of clusters in advance.
Multi-Label Classification
A classification approach designed for problems in which a single instance can belong to multiple labels at once.
Partial Least Squares Regression
A regression method that builds target-aware latent components, especially in highly correlated and high-dimensional feature spaces.
Poisson Regression
A regression method that models count data by estimating the expected number of events through a log-link function.
Polynomial Regression
A method that preserves linear model structure while modeling curved relationships through polynomial terms of input variables.
Principal Component Analysis
The most widely used linear dimensionality reduction method that preserves most of the variance in the data.
Prophet
A modern time series tool developed to flexibly model trend, seasonality, and holiday effects.
Random Forest
An ensemble learning method that combines the outputs of multiple decision trees to make more robust predictions.
Random Projection
A computationally efficient method that projects high-dimensional data into a lower-dimensional space while approximately preserving distances.
Random Search
An optimization approach that searches for effective combinations by randomly sampling the hyperparameter space.
Ranking-Based Recommendation
An approach that optimizes recommendation quality by focusing on presenting items in the right order.
Ridge Regression
A regression method with L2 regularization that reduces overfitting by penalizing coefficient magnitude.
Robust Covariance Anomaly Detection
A statistical approach that performs anomaly detection through covariance estimation that is more robust to outliers.
SARIMA
An extended ARIMA model that also includes seasonal components.
Session-Based Recommendation
A recommendation approach based on the behavior flow within the current session rather than long-term user history.
Spectral Clustering
A clustering method that aims to discover complex cluster structures using similarity graphs and eigen decomposition.
Stacking
An ensemble technique that combines the outputs of multiple base models through a higher-level meta-model.
Successive Halving
An optimization approach that evaluates model candidates in stages, eliminates weak ones, and allocates resources to the strongest candidates.
Support Vector Machine
A powerful classification method that aims to separate classes by maximizing the margin between them.
Temporal Cross-Validation
An approach that performs chronological validation in time series problems to prevent future information from leaking into the past.
Tree-Structured Parzen Estimator
A Bayesian search method that proposes new hyperparameter candidates by modeling good and bad regions based on previous trials.
Truncated SVD
A truncated singular value decomposition method used for dimensionality reduction, especially in sparse matrices.
t-SNE
A method focused on preserving local similarity structure when visualizing high-dimensional data in low dimensions.