python 如何求mse
原创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
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