There are several things that can contribute to getting an answer that’s close, but not exactly what you expect when using model.predict with neural networks:

1. Probabilities and approximations: Neural networks often deal in probabilities and approximations rather than exact values. They analyze your input, weigh various possibilities, and provide the most likely outcome based on their training data. This can lead to predictions that are close to the true value, but not always spot-on.

2. Uncertainties in data and model: Both the data used to train the model and the model itself can contain uncertainties. Noise, biases, and incomplete information in the training data can affect the network’s learning and prediction accuracy. Similarly, limitations in the model’s architecture or complexity can also influence its ability to capture subtle nuances in the data.

3. Stochasticity and randomness: Many neural networks involve elements of randomness during training and prediction. This can introduce some variability in the outputs, sometimes leading to slightly different results even for the same input.

4. Interpretability and complexity: Neural networks can be notoriously difficult to interpret due to their complex internal workings. Understanding how the network arrived at a specific prediction can be challenging, making it difficult to pinpoint the exact factors behind an unexpected outcome.

5. Data preprocessing and normalization: Variations in data preprocessing and normalization can also impact predictions. If the input data used for prediction differs significantly from the data used for training, the results might not be as accurate.

Remember: Even though you might not always get the exact answer you expect, neural network predictions can still be very useful. Analyzing the probability of different outcomes and understanding the potential uncertainties can help you make informed decisions based on the available information.

It’s important to consider the context of your problem, the limitations of the data and model, and the inherent uncertainties in machine learning when interpreting the results of model.predict. Be sure to evaluate the model’s performance on a validation set and refine your expectations accordingly.