Machine Learning MCQ - Softmax activation function for multi-class classification

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, What is the use of Softmax activation function in neural network? Which activation function used in the output layer? Which activation function is used to predict the probabilities in multi-class classification? Which is the most preferred activation function in the last layer if the problem is multi-class classification? Why do we need an activation function in neural network?

Machine Learning MCQ - What is the use of Softmax activation function in neural network?

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1. Which of the following functions can be used as an activation function in the output layer if we wish to predict the probabilities of n classes (p₁, p₂, …, p_k) such that sum of p over all n equals to 1?

a) Sigmoid

b) Rectified Linear Unit (ReLU)

c) Tanh

d) Softmax

Answer: (d) Softmax The Softmax activation function is commonly used as an activation function in the case of multi-class classification problems in machine learning. The output of the Softmax is interpreted as the probability of getting each class. Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of each value are proportional to the relative scale of each value in the vector. The most common use of the softmax function in applied machine learning is in its use as an activation function in a neural network model. Specifically, the network is configured to output N values, one for each class in the classification task, and the softmax function is used to normalize the outputs, converting them from weighted sum values into probabilities that sum to one. Each value in the output of the softmax function is interpreted as the probability of membership for each class. [Please refer here for more on Softmax activation function]    Softmax activation function – some facts The softmax function outputs a vector of values that sum to 1.0 that can be interpreted as probabilities of class membership. The Softmax typically used for multi-class classification. The softmax is a non-linear activation function. The Softmax is typically used as the activation function when 2 or more class labels are present in the class membership in the classification of multi-class problems. The Softmax is most commonly used as an activation function for the last layer of the neural network in the case of multi-class classification.

 

  

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What is an activation function in neural network?

Why do we need Softmax activation function in neural network -  in case of multi-class classification

Purpose of Softmax activation function in neural network

Which activation function is used in last layer of neural network in multi-class classification problem?

Softmax is a non-linear activation functions

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