,scoring='neg_root_mean_squared_error' ,cv=cv ,verbose=False ,n_jobs=-1 ,error_score='raise' ) # fmin寻找目标函数最小值,所以评估指标越小越好 return np.mean(abs(validation_loss['test_score'])) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19....
SymbolicRegressor的适应度有三种,都是机器学习里常见的error function: mae: mean absolute error mse: mean squared error rmse: root mean squared error SymbolicTransformer会最大化输出的新特征与目标变量之间的相关系数的绝对值:(并非相关系数本身,因为很大的负相关反而有利于预测) pearson:皮尔逊积矩相关系数(Pears...
validation_loss = cross_validate(reg,X,y ,scoring="neg_root_mean_squared_error" ,cv=cv ,verbose=False ,error_score='raise' #如果交叉验证中的算法执行报错,则告诉我们错误的理由 ) #交叉验证输出的评估指标是负根均方误差,因此本来就是负的损失 #目标函数可直接输出该损失的均值 return np.mean(valida...
importpandasaspd#importnumpyasnpfromsklearn.model_selectionimporttrain_test_splitfromsklearn.ensembleimportRandomForestRegressorfromsklearn.model_selectionimportcross_val_score#交叉验证fromsklearn.metricsimportmean_absolute_errorfromsklearn.metricsimportmean_squared_errorfromsklearn.metricsimportr2_scorefromsklearn...
使用负均方根误差(neg_root_mean_squared_error)作为打分指标,对各个模型进行交叉检验,将所有模型交叉检验结果均值的绝对值保存到列表cv_results_RMSE中。Out:LinearRegression:1345.667893Ridge:1345.574389Lasso:1347.886019RFRegressor:553.466329XGBRegressor:552.122205输出各个模型在训练集上的RMSE结果,结果如下:13.4回归模型...
# 重新训练模型 rf_most_important.fit(train_important,train_labels) # 预测结果 predictions = rf_most_important.predict(test_important) errors = abs(predictions-test_labels) # 评估结果,保留两位小数 print('Mean Absolute Error:',round(np.mean(errors),2),'%') mape = np.mean(100*(errors/test...
CLIPS_NEG Python 复制 CLIPS_NEG = {'explained_variance': -1, 'r2_score': -1, 'spearman_correlation': -1} CLIPS_POS Python 复制 CLIPS_POS = {'log_loss': 1, 'normalized_mean_absolute_error': 1, 'normalized_median_absolute_error': 1, 'normalized_root_mean_squared_error': 1...
# Evaluate the mean squared error mse = ((func(x) - (2 * x**3 - 3 * x**2 + 4 * x - 1))**2 for x in points)return math.fsum(mse) / len(points),定义一个函数来创建 toolbox。 为了在此处创建toolbox,需要创建一组原语。 这些原语是将在演化过程中使用的运算符。 它们是个体的...
Mean Squared Log Error (MSLE) ✓ ✓ ✓ ✓ Normalized Gini ✓ Quadratic Weighted Kappa ✓ ✓ ✓ Relative Absolute Error (RAE) ✓ Root Mean Squared Error (RMSE) ✓ ✓ ✓ ✓ Relative Squared Error (RSE) ✓ Root Relative Squared Error (RRSE) ✓ Root Mean Squared...
dict_keys(['explained_variance', 'r2', 'max_error', 'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_absolute_percentage_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_root_mean_squared_error', 'neg_mean_poisson_deviance', 'neg_mean_gamma_dev...