Question

netmeds
Question: 12
Why do we use the sigmoid function for binary classification?
Answer:
The sigmoid function extracts a bounded absolute value from the model's output
The sigmoily function converts the model's output into a real number
None of the Mentioned
O The sigmoid function converts the model's output into a probability
O The sigmoid function is faster to calculate than other functions​

Answer 1

Answer:

The main reason why we use sigmoid function is because it exists between (0 to 1). Therefore, it is especially used for models where we have to predict the probability as an output. Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice. The function is differentiable

Explanation:

Sigmoid function, unlike step function, introduces non-linearity into our neural network model. ... This non-linear activation function, when used by each neuron in a multi-layer neural network, produces a new “representation” of the original data, and ultimately allows for non-linear decision boundary, such as XOR.

Answer 2

Answer:

Sigmoid is equivalent to a 2-element softmax, where the second element is assumed to be zero. Therefore, sigmoid is used for binary classification.

Explanation:

The main reason why we use the sigmoid function for binary classification:-

(i) because it exists between 0 to 1.

(ii) The function is differentiable. As in to find the slope of 2 sigmoid curves.

(iii)The function is monotonic.

(iv) The logistic sigmoid function can cause a neural network.