我正在練習使用 sklearn Pipelines 和 GridSearchCV 在套索和嶺回歸模型中查找 alpha 值的值，我們還使用 SimpleImputer 來處理一些缺失值。
但是，我不斷收到以下回溯錯誤：
ValueError: Invalid parameter alpha for estimator Pipeline
但是，我確信 Sklearn 的 Ridge 和 Lasso 都具有 alpha 引數，并在sklearn docs上進行了雙重檢查。
我哪里錯了？我的代碼如下：
import numpy as np
import pandas as pd
from sklearn import svm, datasets
from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn import metrics
columns = ["sex","length","diam","height","whole","shucked","viscera","shell","age"]
df = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data",names=columns)
y = df.age
X=df.drop(columns=['age'])
num_cols = X.select_dtypes(include=np.number).columns
cat_cols = X.select_dtypes(include=['object']).columns
#create some missing values
for i in range(1000):
X.loc[np.random.choice(X.index),np.random.choice(X.columns)] = np.nan
x_train, x_test, y_train, y_test = train_test_split(X,y, random_state=0, test_size=0.25)
cat_vals = Pipeline([("imputer",SimpleImputer(strategy='most_frequent')), ("ohe",OneHotEncoder(sparse=False, drop='first'))])
num_vals = Pipeline([("imputer",SimpleImputer(strategy='mean')), ("scale",StandardScaler())])
preprocess = ColumnTransformer(
transformers=[
("cat_process", cat_vals, cat_cols),
("num_process", num_vals, num_cols)
]
)
#Create a pipeline with preprocess and a linear regression model
pipeline = Pipeline([("preprocess",preprocess),
("regr",LinearRegression())])
#simple parameter grid, with and without the intercept
param_grid = {
'regr': [LinearRegression()],
"regr__fit_intercept": [True,False]
}
# 1. Update the dictionary to include values for alpha in lasso and ridge regression models. Use np.logspace(-4,2,10).
param_grid['alpha'] = np.logspace(-4,2,10)
param_grid['regr'] = [Lasso(), Ridge()]
# 2. Fit the GridSearchCV on the training data and print the best estimator and score from the search.
gscv = GridSearchCV(pipeline, param_grid, scoring='neg_mean_squared_error', cv=5)
gscv.fit(x_train, y_train)
print(gscv.best_params_)
print(gscv.best_score_)
print(gscv.best_estimator_)
uj5u.com熱心網友回復：
引數名稱應根據管道階段名稱給予前綴，即regr__alpha- 就像您為fit_intercept.
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