XGBoost giving a static prediction of "0.5" randomly

Question

I am using a scikit-learn pipeline with XGBRegressor. Pipeline is working good without any error. When I am prediction with this pipeline, I am predicting the same data multiple times, Sometimes out of random the predictions are coming as 0.5 while the normal prediction range is (1000-10,000)

eg : (1258.2,1258.2,1258.2,1258.2,1258.2,1258.2,0.5,1258.2,1258.2,1258.2,1258.2)

• Input Data is exactly same

• Environment is same

  numeric_transformer = Pipeline(steps=[
          ('imputer', SimpleImputer(strategy='mean')),
          ('scaler', StandardScaler())])
      categorical_transformer = Pipeline(steps=[
          ('imputer',
           SimpleImputer(strategy='constant', fill_value='missing')),
          ('onehot', OneHotEncoder(handle_unknown='ignore'))
      ])
  
  numeric_features = X.select_dtypes(
      include=['int64', 'float64']).columns
  categorical_features = X.select_dtypes(
      include=['object']).columns
  preprocessor = ColumnTransformer(
      transformers=[
          ('num', numeric_transformer, numeric_features),
          ('cat', categorical_transformer, categorical_features)])
  
  # Number of trees
  n_estimators = [int(x) for x in
                  np.linspace(start=50, stop=1000, num=10)]
  # Maximum number of levels in tree
  max_depth = [int(x) for x in np.linspace(1, 32, 32, endpoint=True)]
  # Booster
  booster = ['gbtree', 'gblinear', 'dart']
  # selecting gamma
  gamma = [i / 10.0 for i in range(0, 5)]
  # Learning rate
  learning_rate = np.linspace(0.01, 0.2, 15)
  # Evaluation metric
  #         eval_metric = ['rmse','mae']
  # regularization
  reg_alpha = [1e-5, 1e-2, 0.1, 1, 100]
  reg_lambda = [1e-5, 1e-2, 0.1, 1, 100]
  # Min chile weight
  min_child_weight = list(range(1, 6, 2))
  # Samples
  subsample = [i / 10.0 for i in range(6, 10)]
  colsample_bytree = [i / 10.0 for i in range(6, 10)]
  
  # Create the random grid
  random_grid = {'n_estimators': n_estimators,
                 'max_depth': max_depth,
                 'booster': booster,
                 'gamma': gamma,
                 'learning_rate': learning_rate,
                 #                        'eval_metric' : eval_metric,
                 'reg_alpha': reg_alpha,
                 'reg_lambda': reg_lambda,
                 'min_child_weight': min_child_weight,
                 'subsample': subsample,
                 'colsample_bytree': colsample_bytree
                 }
  
  # Use the random grid to search for best hyperparameters
  # First create the base model to tune
  rf = xgboost.XGBRegressor(objective='reg:squarederror', n_jobs=4)
  # Random search of parameters, using 3 fold cross validation,
  # search across 100 different combinations, and use all available cores
  rf_random = RandomizedSearchCV(estimator=rf,
                                 param_distributions=random_grid,
                                 n_iter=100,
                                 cv=3,
                                 verbose=0,
                                 random_state=42,
                                 n_jobs=4)
  
  pipe = Pipeline(steps=[('preprocessor', preprocessor),
                         ('regressor', rf_random)])
  
  pipe.fit(X, y)
  

What could be the issue?

Answer 1

It is probably because you have Nans or None in your target (y)

Answer 2

If you're getting some unusually low predictions, it's probably indicating that the dependent variable has outliers. I'd suggest you to read about it, and about the different strategies to tackle this problem, or recommendations.

Usually its not a good idea to consider all data samples for your model without outlier removal. This will lead to much worse and non-representative metrics.