SMOTE

SMOTE (Synthetic Minority Over-sampling Technique) is a data augmentation technique used in imbalanced datasets to increase the number of instances in the minority class. It works by creating synthetic samples that are similar to the existing minority class instances. SMOTE selects a minority class instance and finds its k nearest neighbors. It then generates new instances by interpolating between the original instance and its neighbors. The amount of interpolation is controlled by a parameter called the sampling ratio. SMOTE can be applied to both numerical and categorical data. It is a popular technique in machine learning and data mining for improving the performance of classifiers on imbalanced datasets.

Your Previous Searches

Random Picks

Force: In Data Science, force refers to the strength or intensity of a relationship between two or more variables. It is a measure of the degree to which the variables are related and how much they influence each other. Force is often used in corr ... Read More >>
Classes: In data science and artificial intelligence, classes refer to a blueprint for creating objects that encapsulate data and behavior. A class defines a set of attributes and methods that are common to all objects of that class. Attributes are ... Read More >>
Congestion: In Data Science, congestion refers to the situation where the amount of data being transmitted through a network exceeds its capacity to handle it, resulting in a slowdown or complete halt of data transfer. Congestion can occur due to vario ... Read More >>

SMOTE

Your Previous Searches

Random Picks

Top News

Employees were already freaked out about AI — Amazon just proved them right...

Trump’s purges come for the U.S.’ nuclear safety board...

Amazon says it will reduce its workforce as AI replaces human employees | CNN Bu...

Amazon’s Jassy says AI will reduce company’s corporate ranks...

Bank of America is bullish on these 4 under-the-radar AI stocks...