Here is an example of how you might document a simple AI model in Python:

python
Import necessary libraries
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics

Define a function to create and train a simple linear regression model
def create_and_train_model(X, y):

Creates and trains a simple linear regression model.

Args:
X (arraylike): Features of the dataset.
y (arraylike): Target variable.

Returns:
model (LinearRegression): Trained linear regression model.

Split the dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Create a Linear Regression model
model = LinearRegression()

Train the model using the training sets
model.fit(X_train, y_train)

return model, X_test, y_test

Define a function to make predictions and evaluate the model
def make_predictions_and_evaluate(model, X_test, y_test):

Makes predictions using the trained model and evaluates its performance.

Args:
model (LinearRegression): Trained linear regression model.
X_test (arraylike): Test features.
y_test (arraylike): Test target variable.

Returns:
y_pred (arraylike): Predicted values.
mae (float): Mean absolute error of the model.
mse (float): Mean squared error of the model.
rmse (float): Root mean squared error of the model.

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

Evaluate the model
mae = metrics.mean_absolute_error(y_test, y_pred)
mse = metrics.mean_squared_error(y_test, y_pred)
rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))

return y_pred, mae, mse, rmse

Example usage
if __name__ == __main__:
Generate some random data
np.random.seed(0)
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

Create and train a model
model, X_test, y_test = create_and_train_model(X, y)

Make predictions and evaluate the model
y_pred, mae, mse, rmse = make_predictions_and_evaluate(model, X_test, y_test)

Print the results
print(fMean Absolute Error: {mae})
print(fMean Squared Error: {mse})
print(fRoot Mean Squared Error: {rmse})

In this code:

* We define two functions: `create_and_train_model` and `make_predictions_and_evaluate`.
* The `create_and_train_model` function takes in some data, splits it into training and testing sets, creates a linear regression model, trains it on the training data, and returns the trained model along with the test features and target variable.
* The `make_predictions_and_evaluate` function takes in a trained model, test features, and a test target variable, makes predictions on the test data, and evaluates the model's performance using mean absolute error, mean squared error, and root mean squared error.
* In the example usage section, we demonstrate how to use these functions by generating some random data, creating and training a model, making predictions, and evaluating the model's performance、

Commit message for this code:

Add simple linear regression model

* Added create_and_train_model function to create and train a simple linear regression model
* Added make_predictions_and_evaluate function to make predictions and evaluate the model's performance
* Included example usage in the main function