[Python] Python LogisticRegression() and RandomSearch: RuntimeWarning

Working on an assignment to knock out a logistic regression with random search for tuning, but I keep getting runtime warnings. Any idea what i'm doing wrong?

/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/utils/extmath.py:205: RuntimeWarning: divide by zero encountered in matmul
ret = a @ b
/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/utils/extmath.py:205: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/sklearn/utils/extmath.py:205: RuntimeWarning: invalid value encountered in matmul


X = hospital_df_a.drop(columns=['stay_7days'])
y = hospital_df_a['stay_7days']

X_train, X_test, y_train, y_test = train_test_split(
X, y,
test_size=0.2,
stratify=y,
random_state=42)



numeric_columns_if = X_train.select_dtypes(include=[np.number]).columns
cat_columns_if = X_train.select_dtypes(include=['object']).columns

numeric_transformer= Pipeline(
steps=[("imputer", SimpleImputer(strategy="median")),
("scaler",StandardScaler())])

categorical_transformer = OneHotEncoder(handle_unknown="ignore")

preprocessor_if = ColumnTransformer(
transformers=[
("num", numeric_transformer, numeric_columns_if),
("cat", categorical_transformer, cat_columns_if)])

r_param_grid_if = [
# Grid for L1 and L2 penalties
{
'logreg__penalty': ['elasticnet'],
'logreg__solver': ['saga'],
'logreg__l1_ratio': loguniform(0.05, 1.0),
'logreg__C': [0.01, 0.1, 1], # Regularization strengths
'logreg__max_iter': [5000, 10000, 20000],
'logreg__class_weight': ['balanced'] # Handling class imbalance
}, {
'logreg__penalty': ['l1', 'l2'],
'logreg__solver': ['liblinear'],

'logreg__C': [0.01, 0.1, 1], # Regularization strengths
'logreg__max_iter': [5000, 10000, 20000],
'logreg__class_weight': [None, 'balanced']
}]

model = Pipeline([('preprocessor', preprocessor_if),
('logreg',LogisticRegression(tol=1e-4))])

random_search = RandomizedSearchCV(model, param_distributions=r_param_grid_if,
cv=3, scoring='precision', n_jobs=-1,
n_iter=50, verbose=1)

random_search.fit(X_train, y_train)

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