All the 5 fits failed. It is very likely that your model is misconfigured. You can try to debug the ...

It's unfortunate that your model is not performing as expected. Here are some steps you can take to debug the issue:

1. Check your data preprocessing: Make sure that your data is properly preprocessed and formatted before being fed into the model. This includes data cleaning, normalization, and encoding.

2. Check your model architecture: Review your model architecture to ensure that it is appropriate for the task at hand. Consider adjusting the number of layers, activation functions, or optimization algorithms.

3. Check your hyperparameters: Experiment with different hyperparameters such as learning rate, batch size, and number of epochs. These can greatly affect the performance of your model.

4. Check your loss function: Make sure that your loss function is appropriate for your problem. For example, if you're dealing with a classification problem, cross-entropy loss may be more suitable than mean squared error.

5. Use visualization tools: Visualize the training process using tools like TensorBoard to identify potential issues such as overfitting or underfitting.

Remember that debugging a machine learning model can be a time-consuming process, but it's important to take the time to properly diagnose and fix any issues in order to achieve the best possible performance.