标签：mode def dataframe type using without panda div ext
如果这些列具有有用信息（在未丢失的位置），则在删除列时，模型将失去对此信息的访问权限。 此外，如果您的测试数据在您的训练数据没有的地方缺少值，则会导致错误。
data_without_missing_values = original_data.dropna(axis=1) #同时操作tran和test部分 cols_with_missing = [col for col in original_data.columns if original_data[col].isnull().any()] redued_original_data = original_data.drop(cols_with_missing, axis=1) reduced_test_data = test_data.drop(cols_with_missing, axis=1)
默认行为填写了插补的平均值。 统计学家已经研究了更复杂的策略，但是一旦将结果插入复杂的机器学习模型，那些复杂的策略通常没有任何好处。
关于Imputation的一个（很多）好处是它可以包含在scikit-learn Pipeline中。 管道简化了模型构建，模型验证和模型部署。
from sklearn.impute import SimpleImputer my_imputer = SimpleImputer() data_with_imputed_values = my_imputer.fit_transform(original_data)
估算是标准方法，通常效果很好。 但是，估算值可能系统地高于或低于其实际值（未在数据集中收集）。 或者具有缺失值的行可能以某种其他方式看来是唯一的。 在这种情况下，您的模型会通过考虑最初缺少哪些值来做出更好的预测。
# make copy to avoid changing original data (when Imputing) new_data = original_data.copy() # make new columns indicating what will be imputed cols_with_missing = (col for col in new_data.columns if new_data[col].isnull().any()) for col in cols_with_missing: new_data[col + ‘_was_missing‘] = new_data[col].isnull() # Imputation my_imputer = SimpleImputer() new_data = pd.DataFrame(my_imputer.fit_transform(new_data)) new_data.columns = original_data.columns
Example (Comparing All Solutions)
import pandas as pd # Load data melb_data = pd.read_csv(‘../input/melbourne-housing-snapshot/melb_data.csv‘) from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error from sklearn.model_selection import train_test_split melb_target = melb_data.Price melb_predictors = melb_data.drop([‘Price‘], axis=1) # For the sake of keeping the example simple, we‘ll use only numeric predictors. melb_numeric_predictors = melb_predictors.select_dtypes(exclude=[‘object‘]) from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(melb_numeric_predictors, melb_target, train_size=0.7, test_size=0.3, random_state=0) def score_dataset(X_train, X_test, y_train, y_test): model = RandomForestRegressor() model.fit(X_train, y_train) preds = model.predict(X_test) return mean_absolute_error(y_test, preds) # Get Model Score from Dropping Columns with Missing Values cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()] reduced_X_train = X_train.drop(cols_with_missing, axis=1) reduced_X_test = X_test.drop(cols_with_missing, axis=1) print("Mean Absolute Error from dropping columns with Missing Values:") print(score_dataset(reduced_X_train, reduced_X_test, y_train, y_test)) # Get Model Score from Imputation from sklearn.impute import SimpleImputer my_imputer = SimpleImputer() imputed_X_train = my_imputer.fit_transform(X_train) imputed_X_test = my_imputer.transform(X_test) print("Mean Absolute Error from Imputation:") print(score_dataset(imputed_X_train, imputed_X_test, y_train, y_test)) # Get Score from Imputation with Extra Columns Showing What Was Imputed imputed_X_train_plus = X_train.copy() imputed_X_test_plus = X_test.copy() cols_with_missing = (col for col in X_train.columns if X_train[col].isnull().any()) for col in cols_with_missing: imputed_X_train_plus[col + ‘_was_missing‘] = imputed_X_train_plus[col].isnull() imputed_X_test_plus[col + ‘_was_missing‘] = imputed_X_test_plus[col].isnull() # Imputation my_imputer = SimpleImputer() imputed_X_train_plus = my_imputer.fit_transform(imputed_X_train_plus) imputed_X_test_plus = my_imputer.transform(imputed_X_test_plus) print("Mean Absolute Error from Imputation while Track What Was Imputed:") print(score_dataset(imputed_X_train_plus, imputed_X_test_plus, y_train, y_test))
标签：mode def dataframe type using without panda div ext
原文地址：https://www.cnblogs.com/hotsnow/p/9477891.html
