Which component in a neural network represents the strength of the connection between attributes?

• A. Biases
• B. Weights
• C. Activation functions
• D. Neurons

Answer: (B)

In a neural network, the strength of the connection between an input feature and a neuron is captured by a weight. Each input value is multiplied by its weight and then all these products are summed to form the neuron's weighted input; the larger the weight, the more influence that feature has on the neuron's activation. During training, these weights are adjusted to reflect how important each attribute is for predicting the target, effectively learning which inputs matter and by how much. This concept is distinct from biases, which shift the activation threshold; activation functions, which introduce nonlinearity; and the neurons themselves, which are the processing units that compute the weighted sum.