![]() ![]() It should be obvious some questions are better than others. How do you find out the secret in the fewest number of questions? At each turn, you may ask a yes-or-no question, and your opponent must answer truthfully. Your opponent has secretly chosen a subject, and you must figure out what he/she chose. ![]() Let's imagine you are playing a game of Twenty Questions. split_frame ( ratios =, seed = 1234 ) # train a GBM model cars_gbm = H2OGradientBoostingEstimator ( distribution = "multinomial", seed = 1234 ) cars_gbm. asfactor () # split into train and validation sets train, valid = cars. import_file ( "" ) # set the predictor names and the response column name predictors = response = "cylinders" cars = cars. ![]() init () # import the cars dataset: # this dataset is used to classify whether or not a car is economical based on # the car's displacement, power, weight, and acceleration, and the year it was made cars = h2o. Import h2o from import H2OGradientBoostingEstimator h2o. However this option can be changed using auc_type model parameter to any other average type of AUC and AUCPR - MACRO_OVR, WEIGHTED_OVR, MACRO_OVO, WEIGHTED_OVO. The AUCPR calculation is disabled (set to NONE) by default. In case of Multinomial AUCPR only one value need to be specified. Multinomial AUCPR metric can be also used for early stopping and during grid search as binomial AUCPR. Note Macro and weighted average values could be the same if the classes are same distributed. ![]() Result Multinomial AUCPR table could look for three classes like this: The result AUCPR is normalized by sum of all weights. Negative class and \(p(j \cup k)\) is prevalence of class \(j\) and class \(k\) (sum of positives of both classes). \[\frac(j, k)\) is theĪUCPR with class \(j\) as the positive class and class \(k\) as the Using the previous example, run the following to retrieve the AUCPR. As such, AUCPR is recommended over AUC for highly imbalanced data. The AUCPR will be much more sensitive to True Positives, False Positives, and False Negatives than AUC. For imbalanced data, a large quantity of True Negatives usually overshadows the effects of changes in other metrics like False Positives. The Precision Recall curve does not care about True Negatives. The main difference between AUC and AUCPR is that AUC calculates the area under the ROC curve and AUCPR calculates the area under the Precision Recall curve. AUCPR is an average of the precision-recall weighted by the probability of a given threshold. These values are obtained using different thresholds on a probabilistic or other continuous-output classifier. This model metric is used to evaluate how well a binary classification model is able to distinguish between precision recall pairs or points. model_performance ( valid ) perfĪUCPR (Area Under the Precision-Recall Curve) ¶ train ( x = predictors, y = response, training_frame = train, validation_frame = valid ) # retrieve the model performance perf = airlines_gbm. split_frame ( ratios =, seed = 1234 ) # train your model airlines_gbm = H2OGradientBoostingEstimator ( sample_rate =. asfactor () # set the predictor names and the response column name predictors = response = "IsDepDelayed" # split into train and validation sets train, valid = airlines. import_file ( "" ) # convert columns to factors airlines = airlines. init () # import the airlines dataset: # This dataset is used to classify whether a flight will be delayed 'YES' or not "NO" # original data can be found at airlines = h2o. Library ( h2o ) h2o.init () # import the airlines dataset: # This dataset is used to classify whether a flight will be delayed 'YES' or not "NO" # original data can be found at airlines <- h2o.importFile ( "" ) # convert columns to factors airlines <- as.factor ( airlines ) airlines <- as.factor ( airlines ) airlines <- as.factor ( airlines ) airlines <- as.factor ( airlines ) airlines <- as.factor ( airlines ) # set the predictor names and the response column name predictors <- c ( "Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum" ) response <- "IsDepDelayed" # split into train and validation airlines_splits <- h2o.splitFrame ( data = airlines, ratios = 0.8, seed = 1234 ) train <- airlines_splits ] valid <- airlines_splits ] # build a model airlines_gbm <- h2o.gbm ( x = predictors, y = response, training_frame = train, validation_frame = valid, sample_rate = 0.7, seed = 1234 ) # retrieve the model performance perf <- h2o.performance ( airlines_gbm, valid ) perf Saving, Loading, Downloading, and Uploading Models.Computing Model Metrics from General Predictions. ![]()
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