What are the different evaluation metrics used in machine learning?

In the first example, we are going to use accuracy as an evaluation metric in a classification problem in Python. Here is the code: 
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = LogisticRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

In this code, we first split our data into training and testing sets using train_test_split . Then we train a Logistic Regression model on the training set. Next, we make predictions on the test set and calculate the accuracy of the model using the accuracy_score function. Finally, we print out the accuracy of the model. In the second example, we are going to use mean squared error (MSE) as an evaluation metric in a regression problem in Python. Here is the code: 
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Calculate the mean squared error of the model
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")

In this code, we follow a similar process as before by splitting the data, training a Linear Regression model, making predictions, and then calculating the mean squared error using the mean_squared_error function. Finally, we print out the mean squared error of the model.

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