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import numpy as np

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import numpy as np

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import lightgbm as lgb
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
params = {'boosting_type': 'gbdt',
             'objective': 'binary',
             'num_leaves': 40,
             'learning_rate': 0.1,
             'feature_fraction': 0.9
             }
gbm = lgb.train(params,

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import xgboost as xgb
params = {"objective":"binary:logistic",'colsample_bytree': 0.3,'learning_rate': 0.1,
               'max_depth': 5, 'alpha': 10}
model = xgb.XGBClassifier(**params)
model.fit(X_train, y_train)
model.fit(X_train, y_train)
model.score(X_test,y_test)

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from sklearn.ensemble import AdaBoostClassifier
model = AdaBoostClassifier(n_estimators=100)
model.fit(X_train, y_train)
model.score(X_test,y_test)

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# Load the dataset
data = load_iris()
X, y = data.data, data.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define base classifiers
base_classifiers = [
   RandomForestClassifier(n_estimators=100, random_state=42),

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from sklearn.ensemble import BaggingRegressor
bagging = BaggingRegressor(DecisionTreeRegressor())
bagging.fit(X_train, y_train)
model.score(X_test,y_test)

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bagging.fit(X_train, y_train)
bagging.score(X_test,y_test)

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from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
bagging = BaggingClassifier(base_estimator=DecisionTreeClassifier(),n_estimators=10, max_samples=0.5, max_features=0.5)

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lanes_districts = gpd.sjoin(districts, bike_lane, how='inner', predicate='intersects')
lanes_districts