Why lightgbm .predict function has probabilities not between 0 and 1?

I wanna understand why in this code, I get the following results:

# Import necessary libraries
import pandas as pd
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, confusion_matrix
import lightgbm as lgb

# Load Titanic dataset
titanic_data = pd.read_csv('titanic.csv')  # Assuming the dataset is stored in 'titanic.csv'

# Select specific features
selected_features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
titanic_data = titanic_data[selected_features]

# Convert categorical features to numerical using one-hot encoding
titanic_data = pd.get_dummies(titanic_data, columns=['Sex', 'Embarked'], drop_first=True)


# Extract features and target variable
X = titanic_data.drop('Survived', axis=1)
y = titanic_data['Survived']

# Split dataset
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)

# Train an initial LightGBM model
initial_model = lgb.train({}, lgb.Dataset(X_train, label=y_train), 100)

# Make predictions and calculate F1 score
y_pred_initial = initial_model.predict(X_valid)
f1_initial = f1_score(y_valid, (y_pred_initial > 0.5).astype(int))

# Display F1 Score and Confusion Matrix
print(f"Initial F1 Score: {f1_initial}")
print("Confusion Matrix:")
print(confusion_matrix(y_valid, (y_pred_initial > 0.5).astype(int)))

When printing y_pref_initials I get an array of probabilities that has negative values and values greater than 1 : Example:

array([ 0.01546079,  0.17557856,  0.22971758,  1.23292351,  0.60531331,
        1.04524314,  0.7637124 ,  0.0458202 ,  0.63044718,  1.02387605,
        0.6441506 ,  0.15202829,  0.06836975,  0.12113314,  0.19732339,
        0.78233429,  0.37779053,  0.75745862,  0.29348834, -0.08458378,
       -0.07173513,  0.73006681,  0.38585976,  0.09324021, -0.02912595,
       -0.10779946,  0.22953974,  0.24480956])

Why the probability here is not between 0 and 1?

You need to choose the objective function for your project, specially for a classifier (by default it makes a regression model by minimizing mse, see doc):

params = {
    "objective": "binary",
}
initial_model = lgb.train(params, lgb.Dataset(X_train, label=y_train), 100)