Is it possible to show the training progress of the MultiOutputRegressor in sklearn? When a huge dataset is processed, my program runs a long time and I have no clue how long it will take. I have shortened my program to a minimal working example below.
import numpy as np
from sklearn.multioutput import MultiOutputRegressor
import xgboost as xgb
df = np.arange(50).reshape(10,5)
train = df[:8]
test = df[8:]
X_train = train[:,0:-2]
X_test = test[:,0:-2]
y_train = train[:,-2:]
y_test = test[:,-2:]
eval_set = [(X_test, y_test)]
multioutputregressor = MultiOutputRegressor(xgb.XGBRegressor(eval_set=eval_set, verbose_eval=True))
multioutputregressor.fit(X_train, y_train)
predictions = multioutputregressor.predict(X_test)
print(predictions)