A/B Testing
A/B testing (also called split testing, bucket testing, or an online controlled experiment) is a randomized controlled experiment that compares two variants, a control (A) and a treatment (B), by randomly…
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Showing 1-24 of 24 articles
Anomaly Detection
In machine learning, anomaly detection is the process of identifying data points, events, or observations that deviate significantly from normal patterns in a dataset.
Bioinformatics
Bioinformatics is the branch of science that builds computational methods, databases, and software for storing, searching, and interpreting biological data, above all the sequences of DNA, RNA, and proteins…
Concept drift
Concept drift is the change over time in the statistical relationship between a model's inputs and its target, formally when the joint distribution P(X, Y) (and in the most damaging case the conditional P(Y |…
Confirmation Bias
Confirmation bias is the tendency to search for, interpret, favor, and recall information in ways that confirm one's preexisting beliefs, and in artificial intelligence it appears in three main forms: human…
DBSCAN
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that groups together points packed closely in feature space and labels points in low-density regions…
Dask
Dask is an open-source Python library for parallel and distributed computing that scales the familiar APIs of libraries such as NumPy, pandas, and scikit-learn to process larger-than-memory datasets.
Data Analysis
Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
Data labeling
Data labeling (also called data annotation) is the process of attaching meaningful tags, labels, or metadata to raw data so that machine learning algorithms can learn from it.
DataFrame
A DataFrame is a two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns), in which each column can hold a different data type and arithmetic…
Dimensionality reduction
Dimensionality reduction is the process of transforming data from a high-dimensional space into a lower-dimensional representation that retains as much of the meaningful structure of the original data as…
Feature Set
A feature set is the complete collection of input variables (features, attributes, or predictors) that a machine learning model uses to learn patterns and make predictions.
Matplotlib
Matplotlib is the foundational open-source data visualization library for Python, created by John D. Hunter in 2003, that produces static, animated, and interactive plots and serves as the de-facto plotting…
Multiple sequence alignment
A multiple sequence alignment (MSA) arranges three or more biological sequences, usually proteins, DNA or RNA
Pandas
Pandas is an open-source data analysis and manipulation library for the Python programming language, providing high-performance, flexible data structures designed for working with structured (tabular…
Synthetic Feature
A synthetic feature (also called a constructed feature or derived feature) is a variable created by transforming, combining, or otherwise manipulating one or more existing features in a dataset, rather than…
Tabular models
Tabular models are machine learning systems that learn from data arranged in tables, where each row is a sample and each column is a feature.
TensorBoard
TensorBoard is the open-source visualization toolkit for TensorFlow, described by Google as "a suite of visualization tools to understand, debug, and optimize TensorFlow programs for ML experimentation." It…
Time Series
A time series is a sequence of observations recorded in time order, often at regular intervals: daily retail sales, hourly electricity demand, per-minute server latency, quarterly GDP.
Time Series Analysis
Time series analysis is the statistical study of ordered observations when dependence across time is part of the signal rather than a nuisance to ignore.
UMAP (Uniform Manifold Approximation and Projection)
UMAP (Uniform Manifold Approximation and Projection) is a nonlinear dimensionality reduction technique that compresses high-dimensional data into a low-dimensional map (typically 2 or 3 dimensions) while…
Unsupervised learning
Unsupervised learning is a branch of machine learning in which algorithms identify patterns, structures, and relationships in data without relying on labeled examples or explicit human guidance.
Uplift Modeling
Uplift modeling (also called incremental modeling, true lift modeling, or net modeling) is a set of machine learning and statistical techniques that predict the incremental impact of a treatment or action on…
t-SNE
t-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction technique used primarily for visualizing high-dimensional data in two or three dimensions.