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Binary Classification ==== This is the quick start tutorial for xgboost CLI version. You can also checkout [../../doc/README.md](../../doc/README.md) for links to tutorial in python or R. Here we demonstrate how to use XGBoost for a binary classification task. Before getting started, make sure you compile xgboost in the root directory of the project by typing ```make``` The script runexp.sh can be used to run the demo. Here we use [mushroom dataset](https://archive.ics.uci.edu/ml/datasets/Mushroom) from UCI machine learning repository. ### Tutorial #### Generate Input Data XGBoost takes LibSVM format. An example of faked input data is below: ``` 1 101:1.2 102:0.03 0 1:2.1 10001:300 10002:400 ... ``` Each line represent a single instance, and in the first line '1' is the instance label,'101' and '102' are feature indices, '1.2' and '0.03' are feature values. In the binary classification case, '1' is used to indicate positive samples, and '0' is used to indicate negative samples. We also support probability values in [0,1] as label, to indicate the probability of the instance being positive. First we will transform the dataset into classic LibSVM format and split the data into training set and test set by running: ``` python mapfeat.py python mknfold.py agaricus.txt 1 ``` The two files, 'agaricus.txt.train' and 'agaricus.txt.test' will be used as training set and test set. #### Training Then we can run the training process: ``` ../../xgboost mushroom.conf ``` mushroom.conf is the configuration for both training and testing. Each line containing the [attribute]=[value] configuration: ```conf # General Parameters, see comment for each definition # can be gbtree or gblinear booster = gbtree # choose logistic regression loss function for binary classification objective = binary:logistic # Tree Booster Parameters # step size shrinkage eta = 1.0 # minimum loss reduction required to make a further partition gamma = 1.0 # minimum sum of instance weight(hessian) needed in a child min_child_weight = 1 # maximum depth of a tree max_depth = 3 # Task Parameters # the number of round to do boosting num_round = 2 # 0 means do not save any model except the final round model save_period = 0 # The path of training data data = "agaricus.txt.train" # The path of validation data, used to monitor training process, here [test] sets name of the validation set eval[test] = "agaricus.txt.test" # The path of test data test:data = "agaricus.txt.test" ``` We use the tree booster and logistic regression objective in our setting. This indicates that we accomplish our task using classic gradient boosting regression tree(GBRT), which is a promising method for binary classification. The parameters shown in the example gives the most common ones that are needed to use xgboost. If you are interested in more parameter settings, the complete parameter settings and detailed descriptions are [here](../../doc/parameter.md). Besides putting the parameters in the configuration file, we can set them by passing them as arguments as below: ``` ../../xgboost mushroom.conf max_depth=6 ``` This means that the parameter max_depth will be set as 6 rather than 3 in the conf file. When you use command line, make sure max_depth=6 is passed in as single argument, i.e. do not contain space in the argument. When a parameter setting is provided in both command line input and the config file, the command line setting will override the setting in config file. In this example, we use tree booster for gradient boosting. If you would like to use linear booster for regression, you can keep all the parameters except booster and the tree booster parameters as below: ```conf # General Parameters # choose the linear booster booster = gblinear ... # Change Tree Booster Parameters into Linear Booster Parameters # L2 regularization term on weights, default 0 lambda = 0.01 # L1 regularization term on weights, default 0 f ```agaricus.txt.test.buffer``` exists, and automatically loads from binary buffer if possible, this can speedup training process when you do training many times. You can disable it by setting ```use_buffer=0```. - Buffer file can also be used as standalone input, i.e if buffer file exists, but original agaricus.txt.test was removed, xgboost will still run * Deviation from LibSVM input format: xgboost is compatible with LibSVM format, with the following minor differences: - xgboost allows feature index starts from 0 - for binary classification, the label is 1 for positive, 0 for negative, instead of +1,-1 - the feature indices in each line *do not* need to be sorted alpha = 0.01 # L2 regularization term on bias, default 0 lambda_bias = 0.01 # Regression Parameters ... ``` #### Get Predictions After training, we can use the output model to get the prediction of the test data: ``` ../../xgboost mushroom.conf task=pred model_in=0003.model ``` For binary classification, the output predictions are probability confidence scores in [0,1], corresponds to the probability of the label to be positive. #### Dump Model This is a preliminary feature, so far only tree model support text dump. XGBoost can display the tree models in text files and we can scan the model in an easy way: ``` ../../xgboost mushroom.conf task=dump model_in=0003.model name_dump=dump.raw.txt ../../xgboost mushroom.conf task=dump model_in=0003.model fmap=featmap.txt name_dump=dump.nice.txt ``` In this demo, the tree boosters obtained will be printed in dump.raw.txt and dump.nice.txt, and the latter one is easier to understand because of usage of feature mapping featmap.txt Format of ```featmap.txt: <featureid> <featurename> <q or i or int>\n ```: - Feature id must be from 0 to number of features, in sorted order. - i means this feature is binary indicator feature - q means this feature is a quantitative value, such as age, time, can be missing - int means this feature is integer value (when int is hinted, the decision boundary will be integer) #### Monitoring Progress When you run training we can find there are messages displayed on screen ``` tree train end, 1 roots, 12 extra nodes, 0 pruned nodes ,max_depth=3 [0] test-error:0.016139 boosting round 1, 0 sec elapsed tree train end, 1 roots, 10 extra nodes, 0 pruned nodes ,max_depth=3 [1] test-error:0.000000 ``` The messages for evaluation are printed into stderr, so if you want only to log the evaluation progress, simply type ``` ../../xgboost mushroom.conf 2>log.txt ``` Then you can find the following content in log.txt ``` [0] test-error:0.016139 [1] test-error:0.000000 ``` We can also monitor both training and test statistics, by adding following lines to configure ```conf eval[test] = "agaricus.txt.test" eval[trainname] = "agaricus.txt.train" ``` Run the command again, we can find the log file becomes ``` [0] test-error:0.016139 trainname-error:0.014433 [1] test-error:0.000000 trainname-error:0.001228 ``` The rule is eval[name-printed-in-log] = filename, then the file will be added to monitoring process, and evaluated each round. xgboost also supports monitoring multiple metrics, suppose we also want to monitor average log-likelihood of each prediction during training, simply add ```eval_metric=logloss``` to configure. Run again, we can find the log file becomes ``` [0] test-error:0.016139 test-negllik:0.029795 trainname-error:0.014433 trainname-negllik:0.027023 [1] test-error:0.000000 test-negllik:0.000000 trainname-error:0.001228 trainname-negllik:0.002457 ``` ### Saving Progress Models If you want to save model every two round, simply set save_period=2. You will find 0002.model in the current folder. If you want to change the output folder of models, add model_dir=foldername. By default xgboost saves the model of last round. #### Continue from Existing Model If you want to continue boosting from existing model, say 0002.model, use ``` ../../xgboost mushroom.conf model_i