I have written the following code to perform `RandomizedSearchCV`

on LightGBM Classifier Model, but I am getting the following error.

`ValueError: For early stopping, at least one dataset and eval metric is required for evaluation`

**Code**

```
import lightgbm as lgb
fit_params={"early_stopping_rounds":30,
"eval_metric" : 'f1',
"eval_set" : [(X_val,y_val)],
'eval_names': ['valid'],
'verbose': 100,
# 'categorical_feature': 'auto'
}
from scipy.stats import randint as sp_randint
from scipy.stats import uniform as sp_uniform
param_test ={'num_leaves': sp_randint(6, 50),
'min_child_samples': sp_randint(100, 500),
'min_child_weight': [1e-5, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4],
'subsample': sp_uniform(loc=0.2, scale=0.8),
'colsample_bytree': sp_uniform(loc=0.4, scale=0.6),
'reg_alpha': [0, 1e-1, 1, 2, 5, 7, 10, 50, 100],
'reg_lambda': [0, 1e-1, 1, 5, 10, 20, 50, 100]}
n_HP_points_to_test = 100
from sklearn.model_selection import RandomizedSearchCV
#n_estimators is set to a "large value". The actual number of trees build will depend on early stopping and 5000 define only the absolute maximum
clf = lgb.LGBMClassifier(max_depth=-1,
random_state=42,
silent=True,
metric='f1',
n_jobs=4,
n_estimators=5000,
)
gs = RandomizedSearchCV(
estimator=clf, param_distributions=param_test,
n_iter=n_HP_points_to_test,
scoring='f1',
cv=3,
refit=True,
random_state=41,
verbose=True)
gs.fit(X_trn, y_trn, **fit_params)
print('Best score reached: {} with params: {} '.format(gs.best_score_, gs.best_params_))
```

**Tried Solutions**

I have tried to implement the solutions given in the following links, but none of them worked. How to fix this?

- LightGBM error : ValueError: For early stopping, at least one dataset and eval metric is required for evaluation
- ValueError: For early stopping, at least one dataset and eval metric is required for evaluation #3028
- For early stopping, at least one dataset and eval metric is required for evaluation #1597

Source: Python Questions