Gal Normal

Hey, how do we know if our neural network is doing well?

Geek Curious

We can evaluate the results using various metrics and techniques!

Gal Happy

Awesome! Teach me how to do that!

Step 1: Separating Training and Testing Data

Gal Excited

So, what's the first thing we need to do?

Geek Smiling

First, we need to split our data into two sets: one for training and another for testing.

Step 2: Calculating the Prediction Accuracy

Gal Wondering

What do we do after testing the neural network?

Geek Happy

We'll calculate the prediction accuracy by comparing the network's output with the actual output for the test data.

Step 3: Using Other Evaluation Metrics

Gal Curious

Are there other ways to evaluate the results?

Geek Explaining

Yes, there are! Depending on the problem, we can use other metrics like precision, recall, F1-score, or the mean squared error.

Step 4: Analyzing the Results

Gal Surprised

How do we know if our neural network is good enough?

Geek Smiling

We'll analyze the results and compare them to a baseline or other models. If our network performs better, then it's doing well!

Step 5: Improving the Model

Gal Troubled

What if our neural network isn't doing so great?

Geek Encouraging

Don't worry! We can improve it by tweaking the network's architecture, adjusting learning parameters, or using more training data.

Step 6: Visualizing the Results

Gal Eager

Can we visualize the results to better understand our neural network?

Geek Nodding

Absolutely! We can use various visualization techniques like confusion matrices, ROC curves, or loss plots to better understand our network's performance.


Now you know how to evaluate the results of a simple neural network! Remember to separate training and testing data, calculate prediction accuracy, use other evaluation metrics, analyze the results, improve the model, and visualize the results. Keep up the good work! 🎉