What are the different evaluation metrics used in machine learning?

In the first example, we are going to demonstrate how to use accuracy as an evaluation metric in machine learning using Python. Here is the code: 
from sklearn.metrics import accuracy_score
y_true = [0, 1, 1, 0, 1]
y_pred = [0, 1, 0, 0, 1]

accuracy = accuracy_score(y_true, y_pred)
print("Accuracy:", accuracy)

Step 1: Import the accuracy_score function from sklearn.metrics. Step 2: Define two lists, y_true and y_pred, containing the true and predicted values. Step 3: Calculate the accuracy score by calling the accuracy_score function with y_true and y_pred as arguments. Step 4: Print out the accuracy score. In the second example, we are going to demonstrate how to use precision and recall as evaluation metrics in machine learning using Python. Here is the code: 
from sklearn.metrics import precision_score, recall_score
y_true = [0, 1, 1, 0, 1]
y_pred = [0, 1, 0, 0, 1]

precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)

print("Precision:", precision)
print("Recall:", recall)

Step 1: Import the precision_score and recall_score functions from sklearn.metrics. Step 2: Define two lists, y_true and y_pred, containing the true and predicted values. Step 3: Calculate the precision and recall scores by calling the precision_score and recall_score functions with y_true and y_pred as arguments. Step 4: Print out the precision and recall scores.

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