In supervised learning, we can understand results using different methods.

Classification:

• Confusion Matrix: This is a table that shows how many times the model got things right and wrong. It includes:

  • True Positives (TP): Correct positive predictions.
  • True Negatives (TN): Correct negative predictions.
  • False Positives (FP): Incorrect positive predictions.
  • False Negatives (FN): Incorrect negative predictions.

• Precision: This measures how good the model is at making positive predictions. It’s calculated like this:

  • Precision = True Positives / (True Positives + False Positives)

• Recall: This checks how well the model finds all the positive examples. It’s calculated like this:

  • Recall = True Positives / (True Positives + False Negatives)

Regression:

• Scatter Plots: These are graphs that show how two things are related to each other.

• R-squared (R²): This number tells us how well the model explains the data we have. It ranges from 0 to 1, where 1 means a perfect fit.

• Mean Absolute Error (MAE): This measures how off the model's predictions are from the real results. It’s calculated like this:

  • MAE = (Sum of the absolute differences between actual values and predicted values) / (Total number of predictions)

These methods help us see how well our models are doing when we try to predict outcomes!