A neural network with one hidden unit and linear activation is linear regression. There may be differences though, for example, neural networks are usually trained with variants of gradient descent, while linear regression with ordinary least squares, so you have no guarantees that they end up with the same results. There also may be implementation details that differ. If you use regularization, other activation, loss, etc those would be different models so again you have no guarantees of finding the same solution, or an equally good one. Unless both models ate exactly the same, you don't really have such guarantees. Linear regression may outperform neural networks for regression problems, or logistic regression can for classification.

A neural network with one hidden unit and linear activation is linear regression. There may be differences though, for example, neural networks are usually trained with variants of gradient descent, while linear regression with ordinary least squares, so you have no guarantees that they end up with the same results. There also may be implementation details that differ. If you use regularization, other activation, loss, etc those would be different models so again you have no guarantees of finding the same solution, or an equally good one. Unless both models are exactly the same, you don't really have guarantees of same performance. Because all of the above reasons, linear regression may outperform neural networks for regression problems, or logistic regression can for classification.

A neural network with one hidden unit and linear activation is linear regression. There may be differences though, for example, neural networks are usually trained with variants of gradient descent, while linear regression with ordinary least squares, so you have no guarantees that they end up with the same results. There also may be implementation details that differ. If you use regularization, other activation, loss, etc those would be different models so again you have no guarantees of finding the same solution, or an equally good one. Unless both models ate exactly the same, you don't really have such guarantees. Linear regression may outperform neural networks for regression problems, or logistic regression does this for classification.

A neural network with one hidden unit and linear activation is linear regression. There may be differences though, for example, neural networks are usually trained with variants of gradient descent, while linear regression with ordinary least squares, so you have no guarantees that they end up with the same results. There also may be implementation details that differ. If you use regularization, other activation, loss, etc those would be different models so again you have no guarantees of finding the same solution, or an equally good one. Unless both models ate exactly the same, you don't really have such guarantees. Linear regression may outperform neural networks for regression problems, or logistic regression can for classification.