Disadvantages of neural networks

Good models come at the cost of a few disadvantages:

• They take time to train: Neural networks do take a long time to train; they are generally slower than a linear regression model or a decision tree model, as these basically just do one pass on the data, while, with neural networks, you actually go through many, many iterations.
• The best solution is not guaranteed: You're not guaranteed to find the best solution. This also means that, in addition to running a single neural network through many iterations, you'll also need to run it multiple times using different starting points so that you can try to get closer to the best solution.
• Black boxes: As we discussed earlier, it is hard to decipher what gave a certain output and how.