Overview
Machine learning evaluation is crucial for understanding how well a model performs in real-world scenarios. By using various metrics such as accuracy, confusion matrices, and cross-validation techniques, we can gain insights into a model's strengths and weaknesses. Evaluating models helps ensure the...
Key Terms
Example: If a model predicts 80 out of 100 instances correctly, its accuracy is 80%.
Example: A confusion matrix shows true positives, false positives, true negatives, and false negatives.
Example: An overfitted model performs well on training data but poorly on new data.
Example: K-fold cross-validation splits the data into K subsets and trains the model K times.
Example: If predictions are 3, 5, and 7, and actual values are 2, 5, and 8, MAE is (1+0+1)/3 = 0.67.
Example: If predictions are 3, 5, and 7, and actual values are 2, 5, and 8, MSE is ((1²+0²+1²)/3) = 0.67.