Drop missing values Get names of columns with missing values: cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
Drop columns in training and validation data: reduced_X_train = X_train.drop(cols_with_missing, axis=1) reduced_X_valid = X_valid.drop(cols_with_missing, axis=1)
**
** class sklearn.impute.SimpleImputer(, missing_values=nan, strategy=‘mean’, fill_value=None, verbose=0, copy=True, add_indicator=False)* sklearn.impute.SimpleImputer
Imputation my_imputer = SimpleImputer() imputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train)) imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid))添加链接描述
Imputation removed column names; put them back imputed_X_train.columns = X_train.columns imputed_X_valid.columns = X_valid.columns
One-hot encoding We use the OneHotEncoder class from scikit-learn to get one-hot encodings. There are a number of parameters that can be used to customize its behavior.
We set handle_unknown=‘ignore’ to avoid errors when the validation data contains classes that aren’t represented in the training data, and(我们设置* handle_unknown ='ignore’以避免在验证数据包含训练数据中未表示的类时出错，并且) setting sparse=False ensures that the encoded columns are returned as a numpy array (instead of a sparse matrix).(设置 sparse = False *可确保将编码的列作为numpy数组（而不是稀疏矩阵）返回。)
1.根据数据类型选择特征 select_dtypes(include=[’’]/exclude=[]) 链接1 LabelEncoder Methods
fit(y) Fit label encoder
fit_transform(y) Fit label encoder and return encoded labels
get_params([deep]) Get parameters for this estimator.
inverse_transform(y) Transform labels back to original encoding.
**set_params(params) Set the parameters of this estimator.
transform(y) Transform labels to normalized encoding.
import pandas as pd from sklearn.model_selection import train_test_split # Read the data X_full = pd.read_csv('../input/train.csv', index_col='Id') X_test_full = pd.read_csv('../input/test.csv', index_col='Id') # Remove rows with missing target, separate target from predictors X_full.dropna(axis=0, subset=['SalePrice'], inplace=True) y = X_full.SalePrice X_full.drop(['SalePrice'], axis=1, inplace=True) # Break off validation set from training data X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, train_size=0.8, test_size=0.2, random_state=0) # "Cardinality" means the number of unique values in a column # Select categorical columns with relatively low cardinality (convenient but arbitrary) categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == "object"] # Select numerical columns numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']] # Keep selected columns only my_cols = categorical_cols + numerical_cols X_train = X_train_full[my_cols].copy() X_valid = X_valid_full[my_cols].copy() X_test = X_test_full[my_cols].copy() # Save test predictions to file from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import OneHotEncoder from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error # Preprocessing for numerical data numerical_transformer = SimpleImputer(strategy='constant') # Preprocessing for categorical data categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='most_frequent')), ('onehot', OneHotEncoder(handle_unknown='ignore')) ]) # Bundle preprocessing for numerical and categorical data preprocessor = ColumnTransformer( transformers=[ ('num', numerical_transformer, numerical_cols), ('cat', categorical_transformer, categorical_cols) ]) # Define model model = RandomForestRegressor(n_estimators=100, random_state=0) # Bundle preprocessing and modeling code in a pipeline clf = Pipeline(steps=[('preprocessor', preprocessor), ('model', model) ]) # Preprocessing of training data, fit model clf.fit(X_train, y_train) # Preprocessing of validation data, get predictions preds = clf.predict(X_valid) print('MAE:', mean_absolute_error(y_valid, preds)) numerical_transformer = SimpleImputer(strategy='mean') # Your code here # Preprocessing for categorical data # Your code here categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore')) ]) # Bundle preprocessing for numerical and categorical data preprocessor = ColumnTransformer( transformers=[ ('num', numerical_transformer, numerical_cols), ('cat', categorical_transformer, categorical_cols) ]) # Define model model = RandomForestRegressor(n_estimators=100, random_state=0) # Your code here # Bundle preprocessing and modeling code in a pipeline my_pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model) ]) # Preprocessing of training data, fit model my_pipeline.fit(X_train, y_train) # Preprocessing of validation data, get predictions preds = my_pipeline.predict(X_valid) # Evaluate the model score = mean_absolute_error(y_valid, preds) print('MAE:', score) # Bundle preprocessing and modeling code in a pipeline my_pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model) ]) # Preprocessing of training data, fit model my_pipeline.fit(X_train, y_train) # Preprocessing of validation data, get predictions preds = my_pipeline.predict(X_valid) # Evaluate the model score = mean_absolute_error(y_valid, preds) print('MAE:', score) # Preprocessing of test data, fit model preds_test = my_pipeline.predict(X_test) # Your code here # Save test predictions to file output = pd.DataFrame({'Id': X_test.index, 'SalePrice': preds_test}) output.to_csv('submission.csv', index=False) import pandas as pd from sklearn.model_selection import train_test_split # Read the data train_data = pd.read_csv('../input/train.csv', index_col='Id') test_data = pd.read_csv('../input/test.csv', index_col='Id') # Remove rows with missing target, separate target from predictors train_data.dropna(axis=0, subset=['SalePrice'], inplace=True) y = train_data.SalePrice train_data.drop(['SalePrice'], axis=1, inplace=True) # Select numeric columns only numeric_cols = [cname for cname in train_data.columns if train_data[cname].dtype in ['int64', 'float64']] X = train_data[numeric_cols].copy() X_test = test_data[numeric_cols].copy() from sklearn.ensemble import RandomForestRegressor from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer my_pipeline = Pipeline(steps=[ ('preprocessor', SimpleImputer()), ('model', RandomForestRegressor(n_estimators=50, random_state=0)) ]) from sklearn.model_selection import cross_val_score # Multiply by -1 since sklearn calculates *negative* MAE scores = -1 * cross_val_score(my_pipeline, X, y, cv=5, scoring='neg_mean_absolute_error') print("Average MAE score:", scores.mean()) import pandas as pd from sklearn.model_selection import train_test_split # Read the data X = pd.read_csv('../input/train.csv', index_col='Id') X_test_full = pd.read_csv('../input/test.csv', index_col='Id') # Remove rows with missing target, separate target from predictors X.dropna(axis=0, subset=['SalePrice'], inplace=True) y = X.SalePrice X.drop(['SalePrice'], axis=1, inplace=True) # Break off validation set from training data X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0) # "Cardinality" means the number of unique values in a column # Select categorical columns with relatively low cardinality (convenient but arbitrary) low_cardinality_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == "object"] # Select numeric columns numeric_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']] # Keep selected columns only my_cols = low_cardinality_cols + numeric_cols X_train = X_train_full[my_cols].copy() X_valid = X_valid_full[my_cols].copy() X_test = X_test_full[my_cols].copy() # One-hot encode the data (to shorten the code, we use pandas) X_train = pd.get_dummies(X_train) X_valid = pd.get_dummies(X_valid) X_test = pd.get_dummies(X_test) X_train, X_valid = X_train.align(X_valid, join='left', axis=1) X_train, X_test = X_train.align(X_test, join='left', axis=1) from xgboost import XGBRegressor # Define the model my_model_1 = XGBRegressor(random_state=0) # Your code here # Fit the model my_model_1.fit(X_train,y_train) # Your code here from sklearn.metrics import mean_absolute_error # Get predictions predictions_1 = my_model_1.predict(X_valid) # Your code here # Calculate MAE mae_1 = mean_absolute_error(predictions_1,y_valid) # Your code here # Uncomment to print MAE print("Mean Absolute Error:" , mae_1)