我正在尝试将一个特征的权重分配得比其他特征更高。以下是我的代码。
## Assign weight to High Net Worth feature
cols = list(train_X.columns.values)
# 0 - 1163 --Other Columns
# 1164 --High Net Worth
#Create an array of feature weights
other_col_wt = [1]*1164
high_net_worth_wt = [5]
feature_wt = other_col_wt + high_net_worth_wt
feature_weights = np.array(feature_wt)
# Initialize the XGBClassifier
xgboost = XGBClassifier(subsample = 0.8, # subsample = 0.8 ideal for big datasets
silent=False, # whether print messages during construction
colsample_bytree = 0.4, # subsample ratio of columns when constructing each tree
gamma=10, # minimum loss reduction required to make a further partition on a leaf node of the tree, regularisation parameter
objective='binary:logistic',
eval_metric = ["auc"],
feature_weights = feature_weights
)
# Hypertuning parameters
lr = [0.1,1] # learning_rate = shrinkage for updating the rules
ne = [100] # n_estimators = number of boosting rounds
md = [3,4,5] # max_depth = maximum tree depth for base learners
# Grid Search
clf = GridSearchCV(xgboost,{
'learning_rate':lr,
'n_estimators':ne,
'max_depth':md
},cv = 5,return_train_score = False)
# Fitting the model with the custom weights
clf.fit(train_X,train_y, feature_weights = feature_weights)
clf.cv_results_
我查看了这里的文档和Akshay Sehgal在stackoverflow上对类似问题的回答这里。但是当我使用上述代码时，出现以下错误：
请问有谁能帮我看看我哪里做错了吗？谢谢。