Complete Guide to Parameter Tuning in Xgboost

Table of Contents

1. The XGBoost Advantage
2. Understanding XGBoost Parameters
3. Tuning Parameters

1. The XGBoost Advantage

1. Regularization:
   • Standard GBM implementation has no regularization like XGBoost, therefore it also helps to reduce overfitting.
   • In fact, XGBoost is also known as ‘regularized boosting’ technique.
2. Parallel Processing:
   • XGBoost implements parallel processing and is blazingly faster as compared to GBM.
   • Check this link out to explore further.
   • XGBoost also supports implementation on Hadoop.
3. High Flexibility
   • XGBoost allow users to define custom optimization objectives and evaluation criteria.
   • This adds a whole new dimension to the model and there is no limit to what we can do.
4. Handling Missing Values
   • XGBoost has an in-built routine to handle missing values.
   • User is required to supply a different value than other observations and pass that as a parameter. XGBoost tries different things as it encounters a missing value on each node and learns which path to take for missing values in future.
5. Tree Pruning:
   • A GBM would stop splitting a node when it encounters a negative loss in the split. Thus it is more of a greedy algorithm.
   • XGBoost on the other hand make splits upto the max_depth specified and then start pruning the tree backwards and remove splits beyond which there is no positive gain.
   • Another advantage is that sometimes a split of negative loss say -2 may be followed by a split of positive loss +10. GBM would stop as it encounters -2. But XGBoost will go deeper and it will see a combined effect of +8 of the split and keep both.
6. Built-in Cross-Validation
   • XGBoost allows user to run a cross-validation at each iteration of the boosting process and thus it is easy to get the exact optimum number of boosting iterations in a single run.
   • This is unlike GBM where we have to run a grid-search and only a limited values can be tested.
7. Continue on Existing Model
   • User can start training an XGBoost model from its last iteration of previous run. This can be of significant advantage in certain specific applications.
   • GBM implementation of sklearn also has this feature so they are even on this point.

2. XGBoost Parameters

1. General Parameters: Guide the overall functioning
2. Booster Parameters: Guide the individual booster (tree/regression) at each step
3. Learning Task Parameters: Guide the optimization performed

2.1 General Parameters

1. booster [default=gbtree]
2. silent [default=0]
3. nthread [default to maximum number of threads available if not set]

2.2 Booster Parameters

1. eta [default=0.3]
   • Typical final values to be used: 0.01-0.2
2. min_child_weight [default=1]
   • Defines the minimum sum of weights of all observations required in a child.
   • This is similar to min_child_leaf in GBM but not exactly. This refers to min “sum of weights” of observations while GBM has min “number of observations”.
   • Used to control over-fitting. Higher values prevent a model from learning relations which might be highly specific to the particular sample selected for a tree.
   • Too high values can lead to under-fitting hence, it should be tuned using CV.
3. max_depth [default=6]
   • Used to control over-fitting as higher depth will allow model to learn relations very specific to a particular sample.
   • Typical values: 3-10
4. max_leaf_nodes
   • The maximum number of terminal nodes or leaves in a tree.
   • Can be defined in place of max_depth. Since binary trees are created, a depth of ‘n’ would produce a maximum of 2^n leaves.
   • If this is defined, GBM will ignore max_depth.
5. gamma [default=0]
   • A node is split only when the resulting split gives a positive reduction in the loss function. Gamma specifies the minimum loss reduction required to make a split.
   • Makes the algorithm conservative. The values can vary depending on the loss function and should be tuned.
6. max_delta_step [default=0]
   • In maximum delta step we allow each tree’s weight estimation to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help making the update step more conservative.
   • Usually this parameter is not needed, but it might help in logistic regression when class is extremely imbalanced.
   • This is generally not used but you can explore further if you wish.
7. subsample [default=1]
   • Same as the subsample of GBM. Denotes the fraction of observations to be randomly samples for each tree.
   • Lower values make the algorithm more conservative and prevents overfitting but too small values might lead to under-fitting.
   • Typical values: 0.5-1
8. colsample_bytree [default=1]
   • Similar to max_features in GBM. Denotes the fraction of columns to be randomly samples for each tree.
   • Typical values: 0.5-1
9. colsample_bylevel [default=1]
   • Denotes the subsample ratio of columns for each split, in each level.
   • I don’t use this often because subsample and colsample_bytree will do the job for you. but you can explore further if you feel so.
10. lambda [default=1]
    • L2 regularization term on weights (analogous to Ridge regression)
    • This used to handle the regularization part of XGBoost. Though many data scientists don’t use it often, it should be explored to reduce overfitting.
11. alpha [default=0]
    • L1 regularization term on weight (analogous to Lasso regression)
    • Can be used in case of very high dimensionality so that the algorithm runs faster when implemented
12. scale_pos_weight [default=1]
    • A value greater than 0 should be used in case of high class imbalance as it helps in faster convergence.

2.3 Learning Task Parameters

1. objective [default=reg:linear]
2. eval_metric [ default according to objective ]
3. seed [default=0]

XGBoost Parameters guide: official github

3. Parameter Tuning

2 forms of XGBoost:

1. xgb – this is the direct xgboost library. I will use a specific function “cv” from this library.
2. XGBClassifier – this is an sklearn wrapper for XGBoost. This allows us to use sklearn’s Grid Search with parallel processing in the same way we did for GBM.

Lets define a function which will help us create XGBoost models and perform cross-validation.

def modelfit(alg, dtrain, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50):
    
    if useTrainCV:
        xgb_param = alg.get_xgb_params()
        xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)
        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,
            metrics='auc', early_stopping_rounds=early_stopping_rounds, show_progress=False)
        alg.set_params(n_estimators=cvresult.shape[0])
    
    #Fit the algorithm on the data
    alg.fit(dtrain[predictors], dtrain['Disbursed'],eval_metric='auc')
        
    #Predict training set:
    dtrain_predictions = alg.predict(dtrain[predictors])
    dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]
        
    #Print model report:
    print "\nModel Report"
    print "Accuracy : %.4g" % metrics.accuracy_score(dtrain['Disbursed'].values, dtrain_predictions)
    print "AUC Score (Train): %f" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob)
                    
    feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
    feat_imp.plot(kind='bar', title='Feature Importances')
    plt.ylabel('Feature Importance Score')

General Approach for Parameter Tuning

1. Choose a relatively high learning rate. Generally a learning rate of 0.1 works but somewhere between 0.05 to 0.3 should work for different problems. Determine the optimum number of trees for this learning rate. XGBoost has a very useful function called as “cv” which performs cross-validation at each boosting iteration and thus returns the optimum number of trees required.
2. Tune tree-specific parameters ( max_depth, min_child_weight, gamma, subsample, colsample_bytree) for decided learning rate and number of trees. Note that we can choose different parameters to define a tree and I’ll take up an example here.
3. Tune regularization parameters (lambda, alpha) for xgboost which can help reduce model complexity and enhance performance.
4. Lower the learning rate and decide the optimal parameters.

Step 1: Fix learning rate and number of estimators for tuning tree-based parameters

In order to decide on boosting parameters, we need to set some initial values of other parameters.

1. max_depth = 5 : This should be between 3-10.
2. min_child_weight = 1 : A smaller value is chosen because it is a highly imbalanced class problem and leaf nodes can have smaller size groups.
3. gamma = 0 : A smaller value like 0.1-0.2 can also be chosen for starting.
4. subsample, colsample_bytree = 0.8 : This is a commonly used used start value.
5. scale_pos_weight = 1: Because of high class imbalance.

Lets take the default learning rate of 0.1 here and check the optimum number of trees using cv function of xgboost.

predictors = [x for x in train.columns if x not in [target, IDcol]]
xgb1 = XGBClassifier(
    learning_rate =0.1,
    n_estimators=1000,
    max_depth=5,
    min_child_weight=1,
    gamma=0,
    subsample=0.8,
    colsample_bytree=0.8,
    objective= 'binary:logistic',
    nthread=4,
    scale_pos_weight=1,
    seed=27)
modelfit(xgb1, train, predictors)

Step 2: Tune other params

1. max_depth, min_child_weight
2. gamma
3. subsample, colsample_bytree
4. reg_alpha
5. reg_lambda

param_test1 = {
    'max_depth':range(3,10,2),
    'min_child_weight':range(1,6,2)
}
gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=5,
    min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,
    objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), 
    param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
gsearch1.fit(train[predictors],train[target])
gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_