XG Boost For Begginers

Nowadays, almost all data scientists are a fan of XGboost algorithm because of their scalable and incredible results. XGBoost has been Given good accuracy in Kaggle data science competitions and Analytics vidya Hackathons.

XGBost speed & scalability = Rocket

XGBoost is a Machine learning technique for regression and classification problems. this is a highly effective and widely used machine learning method. this powerful algorithm lies in its scalability and model performance, execution speed.

It is capable of performing the three main forms of gradient boosting (Gradient Boosting (GB), Stochastic GB and Regularized GB) and it is robust enough to support fine-tuning and addition of regularization parameters

The algorithm was developed to efficiently reduce computing time and allocate an optimal usage of memory resources

xgboost used a more regularized model formalization to control over-fitting, which gives it better performance.

Why ensemble learning?

XGBoost is an ensemble learning method. Ensemble learning offers a systematic solution to combine the predictive power of multiple learners. Bagging and boosting are two widely used ensemble learners. Though these two techniques can be used with several statistical models, the most effective usage has been with decision trees.

• The XGBoost Advantage
• Understanding XGBoost
• ParametersTuning Parameters (with Example)

1. The XGBoost Advantage

the performance and science behind its high accuracy is.

Regularization:

• Standard GBM implementation has no regularization like XGBoost, therefore it also helps to reduce overfitting.
• In fact, XGBoost is also known as a ‘regularized boosting‘ technique.

Parallel Processing:

• XGBoost implements parallel processing and is blazingly faster as compared to GBM.

High Flexibility

• XGBoost allows 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.

Handling Missing Values

• XGBoost has an in-built routine to handle missing values.
• The 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 the future.

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.

Built-in Cross-Validation

• XGBoost allows users to run 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.

2. XGBoost Parameters

The overall parameters have been divided into 3 categories by XGBoost authors:

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

General Parameters

These define the overall functionality of XGBoost.

Tuning:

model=XGBClassifier(silent=False,scale_pos_weight=1,learning_rate=0.01, colsample_bytree = 0.4, subsample = 0.8, objective=’binary:logistic’, n_estimators=1000, reg_alpha = 0.3, max_depth=4, gamma=10)

learning_rate: [0 or 0.1]

usually between 0.1 and 0.01. If you’re focused on performance and have time in front of you, decrease incrementally the learning rate while increasing the number of trees.

booster [default=gbtree]

• Select the type of model to run at each iteration. It has 2 options:
• gbtree: tree-based models
• gblinear: linear models

silent [default=0]:

• Silent mode is activated is set to 1, i.e. no running messages will be printed.
• It’s generally good to keep it 0 as the messages might help in understanding the model.

nthread [default to maximum number of threads available if not set]

• This is used for parallel processing and number of cores in the system should be entered
• If you wish to run on all cores, value should not be entered and algorithm will detect automatically

Though there are 2 types of boosters, I’ll consider only tree booster here because it always outperforms the linear booster and thus the later is rarely used.

eta [default=0.3]

• Analogous to learning rate in GBM
• Makes the model more robust by shrinking the weights on each step
• Typical final values to be used: 0.01–0.2

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.

max_depth [default=6]

• The maximum depth of a tree, same as GBM.
• Used to control over-fitting as higher depth will allow model to learn relations very specific to a particular sample.
• Should be tuned using CV.
• Typical values: 3–10

max_leaf_nodes

• The maximum number of terminal nodes or leaves in a tree.
• It 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.

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.

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.

subsample [default=1]

• Same as the subsample of GBM. Denotes the fraction of observations to be randomly sampled for each tree.
• Lower values make the algorithm more conservative and prevent overfitting but too small values might lead to under-fitting.
• Typical values: 0.5–1

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

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.

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.

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

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.

Learning Task Parameters

These parameters are used to define the optimization objective the metric to be calculated at each step.

objective [default=reg:linear]

• This defines the loss function to be minimized. Mostly used values are:
• binary:logistic –logistic regression for binary classification, returns predicted probability (not class)
• multi:softmax –multiclass classification using the softmax objective, returns predicted class (not probabilities)
• you also need to set an additional num_class (number of classes) parameter defining the number of unique classes
• multi:softprob –same as softmax, but returns predicted probability of each data point belonging to each class.

eval_metric [ default according to objective ]

• The metric to be used for validation data.
• The default values are rmse for regression and error for classification.
• Typical values are:
• rmse — root mean square error
• mae — mean absolute error
• logloss — negative log-likelihood
• error — Binary classification error rate (0.5 threshold)
• merror — Multiclass classification error rate
• mlogloss — Multiclass logloss
• auc: Area under the curve

seed [default=0]

• The random number seed.
• Can be used for generating reproducible results and also for parameter tuning.

If you’ve been using Scikit-Learn till now, these parameter names might not look familiar. A good news is that xgboost module in python has an sklearn wrapper called XGBClassifier. It uses sklearn style naming convention. The parameters names which will change are:

1. eta –> learning_rate
2. lambda –> reg_lambda
3. alpha –> reg_alpha

You must be wondering that we have defined everything except something similar to the “n_estimators” parameter in GBM. Well, this exists as a parameter in XGBClassifier. However, it has to be passed as “num_boosting_rounds” while calling the fit function in the standard xgboost implementation.

Regularization:

Fitting the training set too closely can lead to degradation of the model’s generalization ability. Several so-called regularization techniques reduce this overfitting effect by constraining the fitting procedure.

Sources:

1. XGBoost: Hyperparameter optimization for xgboost
2. XGBoost tuning code by Krish naik: Link
3. stat quest well explained: XGBOOST