feature-selection

来源： https://stackoverflow.com/questions/40167425/error-in-calculating-vif-variance-inflation-factor

来源： https://stackoverflow.com/questions/40167425/error-in-calculating-vif-variance-inflation-factor

来源： https://stackoverflow.com/questions/16249625/difference-between-pca-principal-component-analysis-and-feature-selection

来源： https://stackoverflow.com/questions/33376078/python-feature-selection-in-pipeline-how-determine-feature-names

来源： https://stackoverflow.com/questions/59667340/valueerror-input-x-must-be-non-negative-in-python

来源： https://stackoverflow.com/questions/51968769/getting-feature-importance-in-naive-bayes

问题 After running a Variance Threshold from Scikit-Learn on a set of data, it removes a couple of features. I feel I'm doing something simple yet stupid, but I'd like to retain the names of the remaining features. The following code: def VarianceThreshold_selector(data): selector = VarianceThreshold(.5) selector.fit(data) selector = (pd.DataFrame(selector.transform(data))) return selector x = VarianceThreshold_selector(data) print(x) changes the following data (this is just a small subset of the

问题 After running a Variance Threshold from Scikit-Learn on a set of data, it removes a couple of features. I feel I'm doing something simple yet stupid, but I'd like to retain the names of the remaining features. The following code: def VarianceThreshold_selector(data): selector = VarianceThreshold(.5) selector.fit(data) selector = (pd.DataFrame(selector.transform(data))) return selector x = VarianceThreshold_selector(data) print(x) changes the following data (this is just a small subset of the

问题 I've got a dataset containing a lot of missing values (NAN). I want to use linear or multilinear regression in python and fill all the missing values. You can find the dataset here: Dataset I have used f_regression(X_train, Y_train) to select which feature should I use. first of all I convert df['country'] to dummy then used important features then I have used regression but the results Not good. I have defined following functions to select features and missing values: def select_features

问题 I've got a dataset containing a lot of missing values (NAN). I want to use linear or multilinear regression in python and fill all the missing values. You can find the dataset here: Dataset I have used f_regression(X_train, Y_train) to select which feature should I use. first of all I convert df['country'] to dummy then used important features then I have used regression but the results Not good. I have defined following functions to select features and missing values: def select_features