gridsearchcv

问题 This question already has an answer here : Retrieving specific classifiers and data from GridSearchCV (1 answer) Closed 3 days ago . GridSearchCV and RandomizedSearchCV has best_estimator_ that : Returns only the best estimator/model Find the best estimator via one of the simple scoring methods : accuracy, recall, precision, etc. Evaluate based on training sets only I would like to enrich those limitations with My own definition of scoring methods Evaluate further on test set rather than

问题 I'm working with Gaussian processes and when I use the scikit-learn GP modules I struggle to create and optimise custom kernels using gridsearchcv . The best way to describe this problem is using the classic Mauna Loa example where the appropriate kernel is constructed using a combination of already defined kernels such as RBF and RationalQuadratic . In that example the parameters of the custom kernel are not optimised but treated as given. What if I wanted to run a more general case where I

问题 This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 12 days ago . What I am trying to do? I am trying to use StratifiedKFold() in GridSearchCV() . Then, what does confuse me? When we use K Fold Cross Validation, we just pass the number of CV inside GridSearchCV() like the following. grid_search_m = GridSearchCV(rdm_forest_clf, param_grid, cv=5, scoring='f1', return_train_score=True, n_jobs=2) Then, when I will need to use StratifiedKFold() ,

问题 def show3D(searcher, grid_param_1, grid_param_2, name_param_1, name_param_2, rot=0): scores_mean = searcher.cv_results_['mean_test_score'] scores_mean = np.array(scores_mean).reshape(len(grid_param_2), len(grid_param_1)) scores_sd = searcher.cv_results_['std_test_score'] scores_sd = np.array(scores_sd).reshape(len(grid_param_2), len(grid_param_1)) print('Best params = {}'.format(searcher.best_params_)) print('Best score = {}'.format(scores_mean.max())) _, ax = plt.subplots(1,1) # Param1 is

问题 def show3D(searcher, grid_param_1, grid_param_2, name_param_1, name_param_2, rot=0): scores_mean = searcher.cv_results_['mean_test_score'] scores_mean = np.array(scores_mean).reshape(len(grid_param_2), len(grid_param_1)) scores_sd = searcher.cv_results_['std_test_score'] scores_sd = np.array(scores_sd).reshape(len(grid_param_2), len(grid_param_1)) print('Best params = {}'.format(searcher.best_params_)) print('Best score = {}'.format(scores_mean.max())) _, ax = plt.subplots(1,1) # Param1 is

问题 def show3D(searcher, grid_param_1, grid_param_2, name_param_1, name_param_2, rot=0): scores_mean = searcher.cv_results_['mean_test_score'] scores_mean = np.array(scores_mean).reshape(len(grid_param_2), len(grid_param_1)) scores_sd = searcher.cv_results_['std_test_score'] scores_sd = np.array(scores_sd).reshape(len(grid_param_2), len(grid_param_1)) print('Best params = {}'.format(searcher.best_params_)) print('Best score = {}'.format(scores_mean.max())) _, ax = plt.subplots(1,1) # Param1 is

问题 Good morning! I'm new of python, I use Spyder 4.0 to build neural network. In the script below I use the random forest in order to do feature importances. So the values importances are the ones that tell me what is the importance of each features. Unfortunatly I can't upload the dataset, but I can tell you that there are 18 features and 1 label, both are phisical quantyties and it's a regression problem. I want to export in a excel file the variable importances , but when I do it (simply

问题 Good morning! I'm new of python, I use Spyder 4.0 to build neural network. In the script below I use the random forest in order to do feature importances. So the values importances are the ones that tell me what is the importance of each features. Unfortunatly I can't upload the dataset, but I can tell you that there are 18 features and 1 label, both are phisical quantyties and it's a regression problem. I want to export in a excel file the variable importances , but when I do it (simply

问题 I have the code below where I’m trying to use a custom scorer I defined “custom_loss_five” with GridSearchCV to tune hyper parameters. I have the example code below. I also have some sample data. I’m getting an error 'numpy.dtype' object has no attribute 'base_dtype’. I think this is because I’m mixing keras code with sklearn. I’m also using this same “custom_loss_five” function to train a neural network. So that’s why I used keras. If anyone could point out the issue and let me know how to

问题 I am using recurive feature elimination with cross validation (rfecv) as the feature selection technique with GridSearchCV . My code is as follows. X = df[my_features_all] y = df['gold_standard'] x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=0) k_fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=0) clf = RandomForestClassifier(random_state = 42, class_weight="balanced") rfecv = RFECV(estimator=clf, step=1, cv=k_fold, scoring='roc_auc') param_grid = {