用户提问: 代码注释 ai
Ai回答: 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 (array-like): Features of the dataset.
y (array-like): 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 (array-like): Test features.
y_test (array-like): Test target variable.
Returns:
y_pred (array-like): 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
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提问时间: 2025-04-20 20:18:22
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