The space between independent variables and the dependent variable where the model gets trained is called the hidden layer.

• A. Hidden layer
• B. Input layer
• C. Output layer
• D. Feature space

Answer: (A)

In neural networks, the space between the input features and the final prediction is the hidden layer. It’s the intermediate processing stage where neurons apply learned weights to the inputs and pass the results through activation functions to create new, more informative representations. This transformation enables the model to capture complex, nonlinear relationships that a simple linear mapping cannot handle. The input layer merely passes the raw features into the network, and the output layer produces the predictions. The term feature space is a broader concept describing the space of input feature vectors or learned representations, not the actual layer where computation happens. So the described space corresponds to the hidden layer.