Examples of using Binary classification in English and their translations into Chinese
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A problem with two classes is often called a two-class or binary classification problem.
It's also important to understand that standard logistic regression can only be used for binary classification problems.
For example, suppose we have a binary classification problem, class X represents 95% of the data and class Y the other 5%.
Here we assume binary classification and hence depict two possible output states.
The following values can be obtained from the confusion matrix in a binary classification problem.
Some metrics are essentially defined for binary classification tasks(e.g. f1_score, roc_auc_score).
For now, we will focus on the binary classification problem in which\(y\) can take on only two values, 0 and 1.
For example, a binary classification model can be used to predict whether a website comment is spam(e.g., yes or no).
It's typically used for binary classification problems(1 or 0,“yes” or“no”).
The horse tagging task was multi-label classification, and the nudity detection task was binary classification.
It includes algorithms for binary classification, multiclass classification, regression, structured prediction, deep learning, clustering, unsupervised learning, semi-supervised/metric learning, reinforcement learning and feature selection.
Viewed through the lens of multi-task learning, a model trained on ImageNet learns a large number of binary classification tasks(one for each class).
Some of these are restricted to the binary classification case.
When there are only two labels, this is called binary classification.
When there are only two groups, it is called Binary Classification.
In this section, we define a multilayer Perceptron model for binary classification.
If there are only two such labels, one speaks about binary classification.
The average_precision_score function works only in binary classification and multilabel indicator format.
Because the dependent variable sex has two possible values, this is an example of binary classification.
This section discusses strategies for reducing the problem of multiclass classification to multiple binary classification problems.