python 如何求mse

Python中计算MSE的方法

Python是一种高级编程语言，被广泛用于数据分析、机器学习和许多其他领域，均方误差（MSE）是机器学习模型评估中的一个关键指标，用于衡量模型预测的准确性，以下是使用Python计算MSE的方法。

1、使用NumPy库

如果你的数据是NumPy数组，你可以使用NumPy的mean函数来计算MSE，以下是一个例子：

import numpy as np
真实值和预测值
y_true = np.array([1.0, 1.5, 2.0])
y_pred = np.array([1.2, 1.8, 2.2])
计算MSE
mse = np.mean((y_true - y_pred)  2)
print(f"MSE: {mse}")

2、使用scikit-learn库

scikit-learn是一个流行的机器学习库，它也提供了计算MSE的功能，你可以在model_selection模块中的cross_val_score函数中使用scoring='neg_mean_squared_error'来评估模型的性能。

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression
生成数据
X, y = make_regression(n_samples=100, n_features=1, noise=0.1)
创建并训练模型
model = LinearRegression()
model.fit(X, y)
计算MSE
mse = -cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')
print(f"MSE: {mse}")

3、使用TensorFlow或Keras

如果你正在使用TensorFlow或Keras进行深度学习，你可以使用tf.keras.metrics.MeanSquaredError来计算MSE。

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import MeanSquaredError
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from tensorflow.keras import Input, Model
from tensorflow.keras.metrics import MeanSquaredError as MSLE
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()  # For beautiful visualization set. 
%matplotlib inline  # To display matplotlib graphs inline in Jupyter notebook. 
plt.figure(figsize=(12, 8))  # Set figure size for matplotlib plots. 12x8 inches in width and height respectively. 
plt.style.use('seaborn-whitegrid')  # Using 'seaborn' style with 'whitegrid' theme for matplotlib plots. 
plt.rcParams['font.size'] = 14  # Set font size for matplotlib plots. 14 points in size. 
plt.rcParams['figure.figsize'] = (12, 8)  # Set figure size for matplotlib plots. 12x8 inches in width and height respectively. 
plt.rcParams['axes.labelsize'] = 14  # Set label size for matplotlib plots' axis labels to be 14 points in size. 
plt.rcParams['xticks.labelsize'] = 12  # Set label size for matplotlib plots' x axis tick labels to be 12 points in size. 
plt.rcParams['yticks.labelsize'] = 12  # Set label size for matplotlib plots' y axis tick labels to be 12 points in size. 
plt.rcParams['legend.fontsize'] = 12  # Set font size for matplotlib plots' legend to be 12 points in size. 
plt.rcParams['font.weight'] = 'normal'  # Set font weight for matplotlib plots to be 'normal'. 
plt.rcParams['gridspec.top'] = 0.95  # Set top of the figure window to be at position 0.95 (in fraction of figure height). 
plt.rcParams['gridspec.bottom'] = 0.05  # Set bottom of the figure window to be at position 0.05 (in fraction of figure height). 
plt.rcParams['gridspec.left'] = 0.05  # Set left of the figure window to be at position 0.05 (in fraction of figure width). 
plt.rc