Ensemble Learning
Ensemble learning integrates predictions from many machine learning algorithms to improve prediction accuracy. It works on the fundamental principle that predictions are made with more
Category: Lexicon
Explorer
It is an application preview web app. Explorer in AI: Applications The major examples of the use of Explorer in AI include the following: 1.
F score
F score, also called F-measure or F-1 score, is a measurable parameter used in data science to measure a binary classification model’s accuracy. The F1
Facial Recognition
Facial recognition is a technique or a technology that utilizes algorithms to find and identify human faces in pictures or videos. It can be used
False Negative
A false negative in data science is when a model predicts a specific condition as negative or not present when it is usually positive or
Feedforward Propagation
Feedforward propagation is the simplest form of neural network in which data flows in the forward direction from the input layer to the output layer
Generalization
Generalization refers to an ML model’s capability of accurately performing on new and unknown data points previously not used during the training phase. Essentially, a
Generalization Curve
In Artificial Intelligence, the Generalization Curve is a graphical representation depicting an ML model’s performance on the training and validation datasets plotted as a function
Generative Adversarial Networks (GANs)
A type of neural network that can generate photographs that are almost realistic-looking to the human eye on a superficial level. GAN works by combining
Graphics Processing Unit (GPU)
A Graphics Processing Unit (GPU) is a dedicated electronic circuit that enables the faster rendering of images by rapidly altering and manipulating memory. GPUs achieve
Hallucination
In the field of artificial intelligence (AI), the term “hallucination” refers to the phenomenon that occurs when a machine learning model produces outputs that are
Hashing
Hashing is a method used in machine learning for grouping categorical data, and it is especially useful when the entire variety of categories is huge,
