Model Evaluation - Recall

• Definition

• Calculation

• How to code

Recall is a classification metric used to measure your model’s performance.  It’s represented by the total number of true positives divided by the total number of actual positives. Recall is described as a way of remembering or making insights based on the past. For a binary classification model, where there are only two choices for a prediction, recall is also synonymous with sensitivity. 

A high recall value means that a model is very sensitive to changes and will adapt well.

While a low recall model will struggle with spontaneous changes in user behavior and will do poorly when making predictions on new data.

In our 

, our true positives lie in the first quadrant, and our actual positives are the sum of the False Negatives and True Positives.

Using this example, we can calculate it as (515/ 515+32). Hence we get a recall of 94.14%.

Recall can be calculated by scratch or using a library called SKLearn.

Let’s start by calculating the true positives, which is when the model successfully predicted a positive value. In this case, 1 will be positive and 0 will be negative.

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# Calculate True Positives
true_pos = 0
for i in range(0, total):
  # Is a match, and is positive
 if (y_pred[i] == y_true[i]) and (y_true[i] == 1): 
      true_pos+=1

Next, we’ll calculate the actual positives, this is when the result is supposed to be positive, aka 1.

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# Calculate Actual Positives
actual_pos = 0
for i in range(0, total):
 # True value is positive
  if y_true[i] == 1:
   actual_pos += 1

Finally, we’ll use the two values to calculate the metrics for recall.

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print("True Positives:", true_pos)
print("Actual Positives:", actual_pos)
print("Recall:", true_pos / actual_pos)

SKLearn or SciKitLearn, is a Python library that handles calculating the precision of a model using the 

recall_score

method.

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from sklearn.metrics import recall_score
y_pred = [1, 0, 1, 0]
y_true = [0, 1, 1, 0]
recall_score(y_true, y_pred)

• Confusion matrix (Beginner)

• Sensibility (Intermediate)

• F1-Score (Advanced)

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