# Evaluating a Model

### Precision, Recall, and Confusion Matrix

We have learned about various ML models, but how do we evaluate them? For regression, we can use the difference between the actual and the predicted values — Root Mean Square Error, RMSE, or ordinary least square method, to be more precise — but what about classification models?

One might think that accuracy is a good enough measure to evaluate the goodness of a model. Accuracy is a very important evaluation measure, but it might not be the best metric all the time. Let’s understand this with an example.

#### The Accuracy Trap

Say we are building a model that predicts if patients have a chronic illness. We know that only 0.5% of the patients have the disease, or are “Positive” cases. Now, a dummy model could always give “Negative” as a default result and still have a high accuracy (99.5%!) because our dataset is skewed. Out of all the patients only 0.5% have the disease, so by giving “Negative” as a default answer for 100% of the cases, the model is still able to get the predictions right in 99.5% of the cases – we have a model with a very high accuracy! But is this of any good? Absolutely not! And this is where some other performance measures come into play.

#### Precision, Recall, and Confusion Matrix

Before we talk about these measures, let’s understand a few terms:

1. TP / True Positive: the case was positive, and it was predicted as positive
2. TN / True Negative: the case was negative, and it was predicted as negative
3. FN / False Negative: the case was positive, but it was predicted as negative
4. FP / False Positive: the case was negative, but it was predicted as positive

Since pictures help us to remember things better:

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