2-prediction-model-training-result-cases.txt

The training result in the terminal shows the output of a machine learning model training process using TensorFlow and Keras. Here's a breakdown of the key parts:

TensorFlow Initialization Messages:

TensorFlow is informing you about the use of oneDNN custom operations and CPU optimizations.
Training Epochs:

The model is trained over 20 epochs. Each epoch represents one complete pass through the training dataset.
For each epoch, the following metrics are displayed:
accuracy: The accuracy of the model on the training data.
loss: The loss value on the training data.
val_accuracy: The accuracy of the model on the validation data.
val_loss: The loss value on the validation data.
Warnings:

There are warnings about the use of input_shape/input_dim in Sequential models.
Warnings from sklearn about precision being ill-defined for labels with no predicted samples.
Final Evaluation:

After training, the model is evaluated on a test set.
The classification report shows precision, recall, and f1-score for each class (0 and 1).
precision: The ratio of true positive predictions to the total predicted positives.
recall: The ratio of true positive predictions to the total actual positives.
f1-score: The harmonic mean of precision and recall.
support: The number of true instances for each class.
The overall accuracy of the model is 0.91 (91%).
Model Saving Warning:

A warning about saving the model in HDF5 format, suggesting using the native Keras format instead.
Summary
The model has high accuracy (91%) but struggles with class imbalance, as indicated by the low precision and recall for class 1.
The training and validation loss values fluctuate, indicating potential overfitting or underfitting.
Consider addressing class imbalance and improving model performance on the minority class.