XGBoost is an advanced gradient boosting tree library. XGboost is natively integrated into DSS virtual machine learning, meaning that you can train XGBoost models without writing any code or using any custom model.
In this Howto, we are going to cover advanced optimization techniques that can help you go even further with your XGboost models, by using custom Python recipes (or Jupyter notebooks).
We assume that you are already familiar with how to train a model using Python code (for example with scikit-learn).
XGBoost has a large number of advanced parameters, which can all affect the quality and speed of your model.
max_depth : int Maximum tree depth for base learners. learning_rate : float Boosting learning rate (xgb's "eta") n_estimators : int Number of boosted trees to fit. silent : boolean Whether to print messages while running boosting. objective : string Specify the learning task and the corresponding learning objective. nthread : int Number of parallel threads used to run xgboost. gamma : float Minimum loss reduction required to make a further partition on a leaf node of the tree. min_child_weight : int Minimum sum of instance weight(hessian) needed in a child. max_delta_step : int Maximum delta step we allow each tree's weight estimation to be. subsample : float Subsample ratio of the training instance. colsample_bytree : float Subsample ratio of columns when constructing each tree. base_score: The initial prediction score of all instances, global bias. seed : int Random number seed. missing : float, optional Value in the data which needs to be present as a missing value. If None, defaults to np.nan.
You have 2 ways to control overfitting in xgboost:
- Control the model complexity with max_depth, min_child_weight and gamma.
- Add randomness to make training robust to noise with subsample and colsample_bytree.
Using a Sparse matrix
Xgboost can take in input sparse matrix. That's very useful because when you have categorical variables with high cardinality, you can convert them into dummies matrix without being out of memory!
For this we use a python function:
This return a sparse matrix of 3 columns, one by value of VAR_0001:
You can concatenate this matrix with other dummies matrix with the scipy hstack function:
A really cool feature is early stopping. As you are going to learn more and more trees, you will overfit your training dataset. Early stopping enables you to specify a validation dataset and the number of iterations after which the algorithm should stop if the score on your validation dataset didn't increase.
To use it, you can specify in the fit method of the classifier an evaluation set, an evaluation method and the early stopping round number:
Here, we set explicitly the n_estimators to a very large number. In your job log you'll see the score increasing on the dataset you put in the eval_set list:
Note that you can define your own evaluation metric instead.
Viewing features importance.
You can get the features importance easily in clf.booster().get_fscore()where clf is your trained classifier.
For example, we can use this in a Jupyter notebook:
Using Hyperopt for grid searching
Fine-tuning your XGBoost can be done by exploring the space of parameters possibilities. For this task, you can use the
Hyperopt is a Python library for optimizing over awkward search spaces with real-valued, discrete, and conditional dimensions.
Here an exemple of python recipe to use it:
After loading your datasets of training and validation, we define our objective function.
This function trains a model, evaluates it and returns the error on the validation set. We define the space we want to explore: here, we want to try values from 5 to 30 for max_depth, from 1 to 10 for min_child_weight and from 0.8 to 1 for subsample.
Hyperopt will minimise this error in a maximum of 100 experiments.
More documentation about Hyperopt is available .