The activation function used in the final layer of the neural network for breast cancer classification is typically the sigmoid function. The sigmoid function is a non-linear activation function that maps the input values to a range between 0 and 1. It is commonly used in binary classification tasks where the goal is to classify an input into one of two classes, such as benign or malignant in the case of breast cancer classification.
The sigmoid function is defined as:
f(x) = 1 / (1 + e^(-x))
where x is the input to the function. The sigmoid function has a characteristic S-shaped curve and is bounded between 0 and 1. This property makes it suitable for binary classification tasks as it can be interpreted as the probability of the input belonging to the positive class (e.g., malignant).
By using the sigmoid activation function in the final layer of the neural network, the output of the network can be interpreted as the probability of the input belonging to the positive class. For example, if the output of the sigmoid function is 0.8, it can be interpreted as an 80% probability of the input being classified as malignant.
The choice of the sigmoid function as the activation function in the final layer is motivated by its ability to provide a probabilistic interpretation of the network's output. This can be useful in applications where it is important to understand the confidence or certainty of the classification.
It is worth noting that in some cases, alternative activation functions such as the softmax function may be used in the final layer for multi-class classification tasks. However, for binary classification tasks like breast cancer classification, the sigmoid function is commonly used.
The activation function used in the final layer of the neural network for breast cancer classification is the sigmoid function. This function maps the input values to a range between 0 and 1, allowing for a probabilistic interpretation of the network's output.
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