Actor-critic methods
Actor-critic methods are a family of reinforcement learning algorithms that learn two things at once: a parameterized policy, called the actor, which chooses actions, and a value function, called the critic
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Showing 1-18 of 18 articles
CART algorithm
The CART algorithm (Classification And Regression Trees) is a non-parametric supervised learning method that builds a binary decision tree from labelled training data, using Gini impurity to split…
CatBoost
CatBoost is an open-source gradient boosted decision trees library developed by Yandex and released to the public on July 18, 2017 .
Conditional Random Field
A conditional random field (CRF) is a discriminative probabilistic model for structured prediction, used most often to assign a label to every position in an input sequence.
Dynamic Programming
Dynamic programming (DP) is an algorithmic technique that solves a complex problem by breaking it into simpler overlapping subproblems, solving each subproblem once, and storing the result so it is never…
Feature Selection
Feature selection is the process of choosing a subset of the most relevant input variables (features) from a larger candidate pool for use in a machine learning model, with the goal of finding the smallest set…
HNSW
Hierarchical Navigable Small World (HNSW) is an approximate nearest-neighbor search algorithm that indexes high-dimensional vectors in a multi-layer proximity graph and answers similarity queries in expected…
Kalman Filter
The Kalman filter is a recursive algorithm that estimates the hidden state of a dynamic system from a sequence of noisy measurements.
LightGBM
LightGBM (short for Light Gradient-Boosting Machine) is a free and open-source gradient boosting framework that trains ensembles of decision trees on tabular data, originally developed at Microsoft Research by…
Multi-armed bandit
A multi-armed bandit is a sequential decision problem in which a learner repeatedly picks one action from a fixed set, receives a random reward for that action only, and tries to accumulate as much reward as…
Particle Filter
A particle filter is a simulation-based method for estimating the changing, unobserved state of a system from a sequence of noisy observations.
Probabilistic Graphical Model
A probabilistic graphical model (PGM) is a mathematical representation in which a graph expresses the factorization and conditional-independence structure of a probability distribution.
SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE (Synthetic Minority Over-sampling Technique) is a data preprocessing algorithm that fixes class imbalance by creating new synthetic minority-class examples through interpolation between existing minority…
Shapley value
The Shapley value is a rule from cooperative game theory for dividing the payoff of a group among its members.
Simulated annealing
Simulated annealing is a probabilistic method for finding a good approximate solution to a global optimization problem, especially one with many local optima that trap ordinary hill-climbing methods.
Temporal-difference learning
Temporal-difference (TD) learning is a class of model-free reinforcement learning methods that learn value-function estimates by bootstrapping: updating each estimate of how good a state is toward a target…
XGBoost
XGBoost (short for eXtreme Gradient Boosting) is an open-source software library that implements an optimized, distributed version of gradient boosted decision trees, and it is one of the most widely used…
k-Nearest Neighbors
k-Nearest Neighbors (often abbreviated k-NN or KNN) is a non-parametric, instance-based supervised learning algorithm that classifies a new data point by finding the k most similar examples in the training set…