### Answer
The statement is **True**.

### Explanation
Bernoulli trials are a series of experiments where each experiment has exactly two possible outcomes, commonly referred to as "success" and "failure." While Bernoulli trials are fundamentally binary, they can be extended to situations involving multiple classes through various methods.

In multiclass classification, we are often interested in classifying instances into one of many possible categories. While direct application of Bernoulli trials to a multiclass setting isn't straightforward, we can employ techniques such as one-vs-all or softmax regression that inherently rely on principles surrounding Bernoulli distributions.

For instance, in a one-vs-all approach, separate binary classifiers are created for each class. Each classifier outputs a probability of the input belonging to that specific class (interpretable as a Bernoulli trial) while treating the rest as failures. Thus, while Bernoulli trials are primarily designed for binary outcomes, they can still be leveraged in strategies for multiclass problems, affirming the statement's truth.

Question

### Answer
The statement is **True**.

### Explanation
Bernoulli trials are a series of experiments where each experiment has exactly two possible outcomes, commonly referred to as "success" and "failure." While Bernoulli trials are fundamentally binary, they can be extended to situations involving multiple classes through various methods.

In multiclass classification, we are often interested in classifying instances into one of many possible categories. While direct application of Bernoulli trials to a multiclass setting isn't straightforward, we can employ techniques such as one-vs-all or softmax regression that inherently rely on principles surrounding Bernoulli distributions.

For instance, in a one-vs-all approach, separate binary classifiers are created for each class. Each classifier outputs a probability of the input belonging to that specific class (interpretable as a Bernoulli trial) while treating the rest as failures. Thus, while Bernoulli trials are primarily designed for binary outcomes, they can still be leveraged in strategies for multiclass problems, affirming the statement's truth.

Knowee AI · Accepted Answer

### Answer
The statement is **True**.

### Explanation
Bernoulli trials are a series of experiments where each experiment has exactly two possible outcomes, commonly referred to as "success" and "failure." While Bernoulli trials are fundamentally binary, they can be extended to situations involving multiple classes through various methods.

In multiclass classification, we are often interested in classifying instances into one of many possible categories. While direct application of Bernoulli trials to a multiclass setting isn't straightforward, we can employ techniques such as one-vs-all or softmax regression that inherently rely on principles surrounding Bernoulli distributions.

For instance, in a one-vs-all approach, separate binary classifiers are created for each class. Each classifier outputs a probability of the input belonging to that specific class (interpretable as a Bernoulli trial) while treating the rest as failures. Thus, while Bernoulli trials are primarily designed for binary outcomes, they can still be leveraged in strategies for multiclass problems, affirming the statement's truth.

Bernoulli trials applicable to Multiclass classification (many outcomes).Group of answer choicesTrueFalse

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