TERMINOLOGIES

These are some of the terminologies you needed to understand the machine learning.

model

A mathematical representation of a real world process; a predictive model forecasts a future outcome based on past behaviors.

training

The process of creating a model from the training data. The data is fed into the training algorithm, which learn a representation of the problem, and produces a model. Also called as “learning”.

classification

A prediction method that assigns each data point to a predefined category, e.g., a type of operating system.

training set

A dataset used to fond potentially predictive relationships that will be used to create a model.

feature

Also known as an independent variable or a predictor variable, a feature is a observable quantity, recorded and used by a prediction model. You can also engineer features by combining them or adding new information to them.

algorithm

A set of rules used to make a new calculation or solve a problem.

regression

A prediction method whose output is a real number , that is, a value that represents a quantity along a time. Example: predicting the temperature of an engine or the revenue of the company.

target

In statistics, it is called the dependent variable; it is the output of the model or the variable you wish to predict.

test set

A dataset, separated from the training set but with the same structure used to measure and benchmark the performance of the various models.

overfitting

A situation in which a model that is too complex for the data has been trained to predict the target. This leads to an overly specialized model, which make predictions that do not reflect the reality of the underlying relationship between the features and the target.