Machine learning glossary of important terms
The following list is a compilation of important machine learning terms that are useful as you build your custom models in ML.NET.
This documentation refers to ML.NET, which is currently in Preview. Material may be subject to change. For more information, see the ML.NET introduction.
In classification, accuracy is the number of correctly classified items divided by the total number of items in the test set. Ranges from 0 (least accurate) to 1 (most accurate). Accuracy is one of evaluation metrics of the model performance. Consider it in conjunction with precision, recall, and F-score.
Area under the curve (AUC)
In binary classification, an evaluation metric that is the value of the area under the curve that plots the true positives rate (on the y-axis) against the false positives rate (on the x-axis). Ranges from 0.5 (worst) to 1 (best). Also known as the area under the ROC curve, i.e., receiver operating characteristic curve. For more information, see the Receiver operating characteristic article on Wikipedia.
When the data is used to predict a category, supervised machine learning task is called classification. Binary classification refers to predicting only two categories (for example, classifying an image as a picture of either a 'cat' or a 'dog'). Multiclass classification refers to predicting multiple categories (for example, when classifying an image as a picture of a specific breed of dog).
Coefficient of determination
In regression, an evaluation metric that indicates how well data fits a model. Ranges from 0 to 1. A value of 0 means that the data is random or otherwise cannot be fit to the model. A value of 1 means that the model exactly matches the data. This is often referred to as r2, R2, or r-squared.
A measurable property of the phenomenon being measured, typically a numeric (double) value. Multiple features are referred to as a Feature vector and typically stored as
double. Features define the important characteristics of the phenomenon being measured. For more information, see the Feature article on Wikipedia.
Feature engineering is the process that involves defining a set of features and developing software that produces feature vectors from available phenomenon data, i.e., feature extraction. For more information, see the Feature engineering article on Wikipedia.
A parameter of a machine learning algorithm. Examples include the number of trees to learn in a decision forest or the step size in a gradient descent algorithm. Values of Hyperparameters are set before training the model and govern the process of finding the parameters of the prediction function, for example, the comparison points in a decision tree or the weights in a linear regression model. For more information, see the Hyperparameter article on Wikipedia.
The element to be predicted with the machine learning model. For example, the breed of dog or a future stock price.
In classification, an evaluation metric that characterizes the accuracy of a classifier. The smaller log loss is, the more accurate a classifier is.
Mean absolute error (MAE)
Traditionally, the parameters for the prediction function. For example, the weights in a linear regression model or the split points in a decision tree. In ML.NET, a model contains all the information necessary to predict the label of a domain object (for example, image or text). This means that ML.NET models include the featurization steps necessary as well as the parameters for the prediction function.
A feature extraction scheme for text data: any sequence of N words turns into a feature value.
Numerical feature vector
A feature vector consisting only of numerical values. This is similar to
All of the operations needed to fit a model to a data set. A pipeline consists of data import, transformation, featurization, and learning steps. Once a pipeline is trained, it turns into a model.
In classification, the precision for a class is the number of items correctly predicted as belonging to that class divided by the total number of items predicted as belonging to the class.
In classification, the recall for a class is the number of items correctly predicted as belonging to that class divided by the total number of items that actually belong to the class.
A supervised machine learning task where the output is a real value, for example, double. Examples include predicting stock prices. For more information, see the Regression section of the Machine learning tasks topic.
Relative absolute error
Relative squared error
Root of mean squared error (RMSE)
In regression, an evaluation metric that is the square root of the average of the squares of the errors.
Supervised machine learning
A subclass of machine learning in which a desired model predicts the label for yet-unseen data. Examples include classification, regression, and structured prediction. For more information, see the Supervised learning article on Wikipedia.
The process of identifying a model for a given training data set. For a linear model, this means finding the weights. For a tree, it involves identifying the split points.
A pipeline component that transforms data. For example, from text to vector of numbers.
Unsupervised machine learning
A subclass of machine learning in which a desired model finds hidden (or latent) structure in data. Examples include clustering, topic modeling, and dimensionality reduction. For more information, see the Unsupervised learning article on Wikipedia.
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