python中g-mean_score
时间: 2024-05-02 07:22:05 浏览: 279
在Python中,g-mean score指几何平均分数,是分类模型性能评估指标之一。它是将真实值和预测值转换为二进制标签,然后计算分类器的准确性和召回率。g-mean score可以用以下公式计算:
g-mean score = sqrt(TPR * TNR)
其中,TPR是真正类比率(True Positive Rate),也称为灵敏度(Sensitivity),计算公式为TP / (TP + FN),即真实为正例的样本中,被分类器正确识别为正例的比率。TNR是真负类比率(True Negative Rate),也称为特异度(Specificity),计算公式为TN / (TN + FP),即真实为负例的样本中,被分类器正确识别为负例的比率。sqrt表示平方根。
相关问题
from scipy.stats import randint random_params = {'n_estimators': randint(low=80, high=200), 'max_features': randint(low=2, high=8)} forset_reg = RandomForestRegressor(random_state=2020) random_search_forest = RandomizedSearchCV(forest_reg, param_distributions=random_params, n_iter=20, scoring='neg_mean_squared_error', cv=5, random_state=2020) random_search_forest.fit(housing_prepares, housing_label) cvrus = random_search_forest.cv_results_ for mean_score, params in zip(cvrus['mean_test_score'], cvrus['params']): print(np.sqrt(-mean_score), params) print('随机搜索下最佳参数', random_search_forest.best_params_) print('随机搜索下最佳评估器', random_search_forest.best_estimator_) 将以上代码用plot绘图
同样,我无法直接绘制图表,但是你可以将不同超参数下的模型得分绘制成图表进行可视化展示。以下是一个示例代码:
```python
import matplotlib.pyplot as plt
import pandas as pd
# 将 cv_results_ 转化为 DataFrame 格式
cvres_df = pd.DataFrame(random_search_forest.cv_results_)
# 提取所需列
cvres_df = cvres_df[['param_n_estimators', 'param_max_features', 'mean_test_score']]
# 将 param_n_estimators 和 param_max_features 转化为数值类型
cvres_df['param_n_estimators'] = cvres_df['param_n_estimators'].astype('int')
cvres_df['param_max_features'] = cvres_df['param_max_features'].astype('int')
# 绘制图表
fig, ax = plt.subplots(figsize=(12, 8))
for n_estimators in range(80, 201, 20):
df = cvres_df[cvres_df['param_n_estimators'] == n_estimators]
ax.plot(df['param_max_features'], np.sqrt(-df['mean_test_score']), label=f'n_estimators={n_estimators}')
ax.set_xlabel('max_features')
ax.set_ylabel('RMSE')
ax.legend()
plt.show()
```
该代码将不同超参数下的模型得分绘制成了一张折线图,横坐标为 max_features,纵坐标为 RMSE。其中,每种颜色代表不同的 n_estimators 值。你可以根据实际情况进行修改和调整。
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV # 这里对随机森林模型进行参数搜索 param_grid = [ {'n_estimators': [80, 100, 120], 'max_features': [2, 6, 10]}, {'bootstrap': [False], 'n_estimators': [80, 100, 120], 'max_features': [2, 6, 10]} ] forest_reg = RandomForestRegressor() grid_search_forest = GridSearchCV(forest_reg, param_grid=param_grid, scoring='neg_mean_squared_error', cv=5) grid_search_forest.fit(housing_prepares, housing_label) print('网格搜索下最佳参数', grid_search_forest.best_params_) print('网格搜索下最佳评估器', grid_search_forest.best_estimator_) # 不同超参数下的模型得分 cvres = grid_search_forest.cv_results_ for mean_score, params in zip(cvres['mean_test_score'], cvres['params']): print(np.sqrt(-mean_score), params) 将以上代码用plot绘图
抱歉,我作为语言模型无法绘图。但是,你可以将不同超参数下的模型得分绘制成图表进行可视化展示。可以使用 Matplotlib 或 Seaborn 来实现。以下是一个示例代码:
```python
import matplotlib.pyplot as plt
import pandas as pd
# 将 cv_results_ 转化为 DataFrame 格式
cvres_df = pd.DataFrame(grid_search_forest.cv_results_)
# 提取所需列
cvres_df = cvres_df[['param_n_estimators', 'param_max_features', 'mean_test_score']]
# 将 param_n_estimators 和 param_max_features 转化为数值类型
cvres_df['param_n_estimators'] = cvres_df['param_n_estimators'].astype('int')
cvres_df['param_max_features'] = cvres_df['param_max_features'].astype('int')
# 绘制图表
fig, ax = plt.subplots(figsize=(12, 8))
for n_estimators in [80, 100, 120]:
df = cvres_df[cvres_df['param_n_estimators'] == n_estimators]
ax.plot(df['param_max_features'], np.sqrt(-df['mean_test_score']), label=f'n_estimators={n_estimators}')
ax.set_xlabel('max_features')
ax.set_ylabel('RMSE')
ax.legend()
plt.show()
```
该代码将不同超参数下的模型得分绘制成了一张折线图,横坐标为 max_features,纵坐标为 RMSE。其中,每种颜色代表不同的 n_estimators 值。你可以根据实际情况进行修改和调整。
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