Trained an Adaboost Regressor with following parameters :
base_estimator= DecisionTreeRegressor(max_depth = 5)
n_estimators=500
learning_rate= 0.3
random_state = 100
The dataset I am using has 24 features with around 2 million samples. Each feature is an integer.
After the completing the model training on 80% of the data when trying to use the predict and score functions. The function kills the RAM rapidly and system crashed.
What might have been the reason behind this?
from sklearn.ensemble import AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor
model = AdaBoostRegressor(n_estimators=500, base_estimator = DecisionTreeRegressor(max_depth = 5), learning_rate=0.3, loss='square')
model.fit(X_train, y_train)
model.predict(X_test)# This line crashes the RAM very rapidly.