Note that the ``h2o-genmodel.jar`` file, # is a library that supports scoring and contains the required readers, # and interpreters. In the example below, 0 was predicted correctly 902 times, while 8 was predicted correctly 822 times and 0 was predicted as 4 once. This is used for generating the Graphviz format. This section describes how H2O-3 can be used to evaluate model performance. Run the following commands to build a simple GBM model. Models can also be evaluated with specific model metrics, stopping metrics, and performance graphs. Using the previous example, run the following to retrieve the RMSLE value. Unlike AUC which looks at how well a model can classify a binary target, logloss evaluates how close a model’s predicted values (uncalibrated probability estimates) are to the actual target value. This example uses GBM, but any supported algorithm can be used to build a model and run the MOJO. 64 likes. In H2O, the actual results display in the columns and the predictions display in the rows; correct predictions are highlighted in yellow. This parameter specifies that a model must improve its lift within the top 1% of the training data. 128 likes. If there are two columns using user-defined split points, there should be two lists in the nested list. C is the total number of classes (C=2 for binary classification). Let’s continue with our example where our target is to predict the number of days until an event. Note that each tree file is saved as a binary file type. There is one instance, however, where our model did very poorly. Here is an example output for a GBM model: The following options can be specified with PrintMojo: --input (or -i): Required. H2O PRODUCTIONS Fiche entreprise : chiffres d'affaires, bilan et résultat. This defaults to “all”. Les données personnelles dans le cadre des demandes internet sont stockées en vue du traitement de l'information. What kind of technology would I need to use? - To enable it setup system property sys.ai.h2o.auc.maxClasses to a number. The F1 score is calculated from the harmonic mean of the precision and recall. If you have SHAP installed, then graphical representations can be retrieved in Python using SHAP functions. The Lorenz curve plots the true positive rate (y-axis) as a function of percentiles of the population (x-axis). The resulting executable is much smaller and faster than a POJO. Notes More weight should be given to precision for cases where False Positives are considered worse than False Negatives. If you train a model with training and validation data, the max F1 threshold comes from the validation data model. At very small scale (50 trees / 5 depth), POJOs were found to perform ≈10% faster than MOJOs for binomial and regression models, but 50% slower than MOJOs for multinomial models. In case of Multinomial AUCPR only one value need to be specified. The mean absolute error is an average of the absolute errors. To view any of the following details, select it from the drop-down Criterion list: Max absolute MCC (the threshold that maximizes the absolute Matthew’s Correlation Coefficient). negative class. From the “Flow” menu choose the “Run all cells” option. This option is only available in GBM, DRF, and IF. # import H2OGradientBoostingEstimator and the prostate dataset: "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip". The contributions field will provide Shapley contributions. For binary classification problems, H2O uses the model along with the given dataset to calculate the threshold that will give the maximum F1 for the given dataset. L'eau pure industrielle | H2O PRODUCTION est une entreprise spécialiste du traitement de l'eau. Note: MOJO/POJO predict cannot parse columns enclosed in double quotes (for example, “”2””). #TPMP + Primes / L'Oeuf ou la Poule / Est-ce que ça marche / Show ! Use this instead of RMSE if an under-prediction is worse than an over-prediction. The Lorenz curve represents a collective of models represented by the classifier. #TPMP + Primes / L'Oeuf ou la Poule / Est-ce que ça marche / Show ! H2O Productions est la société de productions de Cyril Hanouna. recall is the positive observations (true positives) the model correctly identified from all the actual positive cases (the true positives + the false negatives). \(| x_i - x |\) equals the absolute errors. The example code below shows how to start H2O Flow, compile and run an example Flow, and then compile and run the POJO. The result AUCPR is normalized by number of all class combinations. // Then before calling the model... call modelparams._ignored_columns= Array("inq_last_6mths"), "Fields to add to _ignored_columns field", hex.genmodel.easy.EasyPredictModelWrapper, "Has penetrated the prostatic capsule (1=yes; 0=no): ", "http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", # In another terminal window, download and extract the, # latest stable h2o.jar from http://www.h2o.ai/download/. Usually our model is very good. Vous devez écrire l'équation ajustée de la réaction de combustion (supposée complète c-à-d production de C02 et de H2O) de l'heptane et de l'octane. Remember to change the IP address. negative class and \(p(j \cup k)\) is prevalence of class \(j\) and class \(k\) (sum of positives of both classes). The partial dependence of a given feature \(X_j\) is the average of the response function \(g\), where all the components of \(X_j\) are set to \(x_j\) \((X_j = {[x{^{(0)}_j},...,x{^{(N-1)}_j}]}^T)\). The POJO provides just the math logic to do predictions, so you won’t find any Spark (or even H2O) specific code there. 6 Ayant une expérience significative de la saisie comptable et de manière générale un expert de l‘externalisation offshore de saisie de données. p is the predicted value (uncalibrated probability) assigned to a given row (observation). For example, if you are predicting whether a customer will churn, you can take the predicted probabilities and turn them into classes - customers who will churn vs customers who won’t churn. This is taken as a directory name in the case of .png format and multiple trees to visualize. Instead of cols, you can use the col_pairs_2dpdp option along with a list containing pairs of column names to generate 2D partial dependence plots. Yes, but this way of making predictions is separate from the POJO. - Calculation of this metric can be very expensive on time and memory when the domain is big. If you’re using the UI, click the Preview POJO button for your model. Why did I receive the following error when trying to compile the POJO? Internal H2O representation of the decision tree (splits etc.). (You can see “extends GenModel” in a POJO class. Le siège social de cette entreprise est actuellement situé 50 rue Marcel Dassault - 92100 Boulogne billancourt If you want a more robust metric, try mean absolute error (MAE).). The area under the precision-recall curve graph represents how well a binary classification model is able to distinguish between precision recall pairs or points. Voir le profil de Oceane Jossomme sur LinkedIn, le plus grand réseau professionnel mondial. You can deploy a RESTful server on AWS using the marketplace AMI (H2O Inference server - Hourly). The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). where \(c\) is the number of classes and \(\text{AUC}(j, k)\) is the The MCC score provides a measure of how well a binary classifier detects true and false positives, and true and false negatives. The MCC is called a correlation coefficient because it indicates how correlated the actual and predicted values are; 1 indicates a perfect classifier, -1 indicates a classifier that predicts the opposite class from the actual value, and 0 means the classifier does no better than random guessing. good when you want to give more weight to precision, good when you want to give more weight to recall, highly interpretable, bad for imbalanced data. Note: Multinomial classification models are currently not supported. Par la suite, il change d'associé et crée la société « H2O Production » en mars 2010. The following code snippets show an example of H2O building a model and downloading its corresponding POJO from an R script and a Python script. While POJOs continue to be supported, some customers encountered issues with large POJOs not compiling. 54 talking about this. (Note that H2O-3 also calculates regression metrics for Classification problems. Oceane a 5 postes sur son profil. This parameter specifies that a model must improve its misclassification rate by a given amount (specified by the stopping_tolerance parameter) in order to continue iterating. Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall (penalizing the model more for false negatives then false positives). POJOs are not supported for source files larger than 1G. The Gini index is based on the Lorenz curve. The examples are based off of a GBM model built using the allyears2k_headers.zip dataset. For example, if your use case is to predict which products you will run out of, you may consider False Positives worse than False Negatives. save_to (R)/save_to_file (Python): Specify a fully qualified name to an image file that the resulting plot should be saved to, e.g. Certain metrics are more sensitive to outliers. If your MOJO is in S3, assign a role that provides S3 access to the instance. Currently, only a subset of H2O MOJOs can be converted to the ONNX format: supports multinomial distribution with 3 or more classes (use binomial otherwise), does not support poisson, gamma, or tweedie distributions, does not support models with categorical splits. H2O-3 provides a variety of metrics that can be used for evaluating supervised and unsupervised models. Note that this file references the GBM model created above using R. GBM and DRF return classProbabilities, but not all MOJOs will return a classProbabilities field. Ignorer. H2A: Hydrogen Analysis Production Models. H 2 O at Home, ce n'est pas que le propre, c'est aussi le bonheur à la maison !. Choosing this depends on the use of the model. Here is the official trailer of VERSUS. Variables are listed in order of most to least importance. AUCPR with class \(j\) as the positive class and rest classes \(rest_j\) as the This example uses GBM, but any supported algorithm can be used to build a model and run the POJO. For these use cases, it is best to select a metric that does not include True Negatives or considers relative size of the True Negatives like AUCPR or MCC. How do I score new cases in real-time in a production environment? The method of computing each variable’s importance depends on the algorithm. plot_stddev: A boolean specifying whether to add standard error to partial dependence plot. Bilan Gratuit de H2O PRODUCTIONS à BOULOGNE BILLANCOURT (92100) sur SOCIETE.COM (521679407), chiffre d'affaire, résultat net, bénéfices, actif, passif, compte de résultat Transfer the MOJO file into the /tmp folder of this instance before launching. \(F_1,_n\) is the sum of all events observed so far up to the bin i divided by the total number of events. This function predicts against a test frame. Java developers should refer to the Javadoc for more information, including packages. The F1 score provides a measure for how well a binary classifier can classify positive cases (given a threshold value). Deviance is computed as follows: The model will stop building after the mean-per-class error rate fails to improve. It is a nonparametric test that compares the cumulative distributions of two unmatched data sets and does not assume that data are sampled from any defined distributions. Calling a user-defined function directly from hive: See the H2O-3 training github repository. H2O Open Tour 2016 New York City: Ways to Productionize H2O. (Tip: MAE is robust to outliers. The RMSE metric evaluates how well a model can predict a continuous value. Here are our requirements (assuming you are using the “easy” Prediction API for the POJO as described in the Javadoc). The metric is composed of these outputs: One class versus one class (OVO) AUCPRs - calculated for all pairwise AUCPR combination of classes ((number of classes × number of classes / 2) - number of classes results), One class versus rest classes (OVR) AUCPRs - calculated for all combination one class and rest of classes AUCPR (number of classes results), Macro average OVR AUCPR - Uniformly weighted average of all OVR AUCPRs. Here is a collection of example design patterns for how to productionize H2O. (You can see “extends GenModel” in a pojo class. The metric you choose will either evaluate how accurate the probability is or how accurate the assigned class is from that probability. If you want a more robust metric, try mean absolute error (MAE).). Be sure you change the mojofile path below. Using the previous example, run the following to retrieve the MCC value. In this case, you want your predictions to be very precise and only capture the products that will definitely run out. Lift is the ratio of correctly classified positive observations (rows with a positive target) to the total number of positive observations within a group. # While still running H2O in the first terminal window, # Download the latest stable h2o release from http://www.h2o.ai/download/. Click on the Download POJO button as shown in the following screenshot: Note: The instructions below assume that the POJO model was downloaded to the “Downloads” folder. Weighted average OVR AUCPR - Prevalence weighted average of all OVR AUCPRs. This is available in the H2O-3 GitHub repo at: https://github.com/h2oai/h2o-3/tree/master/h2o-genmodel/src/main/java/hex/genmodel/easy/prediction. AUCPR with class \(j\) as the positive class and class \(k\) as the See actions taken by the people who manage and post content. data: (Required) An H2OFrame object used for scoring and constructing the plot. Depending on your area of interest, select a learning path from the sidebar, or look at the full content outline below. Note that for .png output, Java 8 is the minimum Java requirement. - To enable it setup system property sys.ai.h2o.auc.maxClasses to a number of maximum allowed classes. negative class. For POJOs, it contains the base classes from which the POJO is derived from. A ROC Curve is a graph that represents the ratio of true positives to false positives. --output (or -o): Optionally specify the output file name. 7 Pouvant facilement gérer des pics de production (saisonnalité) 8 Disponible avec un ou plusieurs chef(s) de projet(s) attribué(s) Using the previous example, run the following to retrieve the RMSE value. administratrice de production H2O productions Aug 2016 - Jul 2017 1 year. MOJOs are supported for Deep Learning, DRF, GBM, GLM, GAM, GLRM, K-Means, PCA, Stacked Ensembles, SVM, Word2vec, and XGBoost models. In addition to that, you can choose to generate the reconstructed data row as well. # Retrieve the number of occurrences of each feature for given observations, # on their respective paths in a tree ensemble model, Saving, Loading, Downloading, and Uploading Models. The smaller the MSE, the better the model’s performance. (Tip: AUC is usually not the best metric for an imbalanced binary target because a high number of True Negatives can cause the AUC to look inflated. The Hydrogen Analysis (H2A) hydrogen production models and case studies provide transparent reporting of process design assumptions and a consistent cost analysis methodology for hydrogen production at central … H2O conducted in-house testing using models with 5000 trees of depth 25. # The model, along with the **h2o-genmodel.jar** file will. The leafNodeAssignments field will show the decision path through each tree. In this case, the plots are saved to a file instead of being rendered. This can be accomplished in memory or using MOJOs/POJOs. The graph below shows the absolute error in our predictions. --fontsize (or -f): Controls the font size. targets: (Required, multiclass only) Specify an array of one or more target classes when building PDPs for multiclass models. 92100 BOULOGNE BILLANCOURT MOJO predict cannot parse columns enclosed in double quotes (for example, “”2””). Developers can refer to the POJO and MOJO Model Javadoc. This section describes how H2O-3 can be used to evaluate model performance. H2O PRODUCTIONS, SA par action simplifiée à associé unique au capital de 50 000€, a débuté son activité en avril 2010. So it is disabled by default. The GenModel class is part of this library.) H2O Production. H2o Productions. One of either col_pairs_2dpdp or cols must be specified. You can also preview the POJO inside Flow, but it will only show the first thousand lines or so in the web browser, truncating large models. Using short film as a medium to motivate people who watch our production short film. Macro average OVO AUC - Uniformly weighted average of all OVO AUCs. // handy when you have a thousand columns but want to train on only the important ones. Des produits qui changent le quotidien tout en protégeant la planète : démaquillage à l'eau à découvrir absolument, hygiène naturelle, produits safe sains pour bébé, linge de bain, huiles essentielles et parfums naturels. Consultez le profil complet sur LinkedIn et découvrez les relations de Oceane, ainsi que des emplois dans des entreprises similaires. # set the predictors and response column: # import H2OGeneralizedLinearEstimator and the prostate dataset: # set the predictors columns, repsonse column, and distribution type: # build the standardized coefficient magnitudes plot: \((X_j = {[x{^{(0)}_j},...,x{^{(N-1)}_j}]}^T)\). Using the previously imported and split airlines dataset, run the following to retrieve the KS metric. If you remove the one outlier record from our calculation, RMSE drops down significantly. Instances like this will more heavily penalize metrics that are sensitive to outliers. (Note that retrieving graphs via R is not yet supported.) An .ipynb demo showing this example is also available here. (Note that for binary models, labels are based on the maximum F1 threshold from the model object.) Available formats include dot (default), json, raw, and png. An optional Type can also be specified to define the placements. An F2 score ranges from 0 to 1, with 1 being a perfect model. Open a terminal window and start python. where \(c\) is the number of classes, \(\text{AUC}(j, k)\) is the // Execute as a classfile. The order of the rows in the results is the same as the order in which the data was loaded. Production which produce a short film. For deeper trees, “NA” will be returned for paths of length 64 or more (-1 for node IDs). 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. \[MSE = \frac{1}{N} \sum_{i=1}^{N}(y_i -\hat{y}_i)^2\], \[RMSE = \sqrt{\frac{1}{N} \sum_{i=1}^{N}(y_i -\hat{y}_i)^2 }\], \[RMSLE = \sqrt{\frac{1}{N} \sum_{i=1}^{N} \big(ln \big(\frac{y_i +1} {\hat{y}_i +1}\big)\big)^2 }\], \[MAE = \frac{1}{N} \sum_{i=1}^{N} | x_i - x |\], \[MCC = \frac{TP \; x \; TN \; - FP \; x \; FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\], \[F1 = 2 \;\Big(\; \frac{(precision) \; (recall)}{precision + recall}\; \Big)\], \[F0.5 = 1.25 \;\Big(\; \frac{(precision) \; (recall)}{0.25 \; precision + recall}\; \Big)\], \[F2 = 5 \;\Big(\; \frac{(precision) \; (recall)}{4\;precision + recall}\; \Big)\], \[Accuracy = \Big(\; \frac{\text{number correctly predicted}}{\text{number of observations}}\; \Big)\], \[Logloss = - \;\frac{1}{N} \sum_{i=1}^{N}w_i(\;y_i \ln(p_i)+(1-y_i)\ln(1-p_i)\;)\], \[Logloss = - \;\frac{1}{N} \sum_{i=1}^{N}\sum_{j=1}^{C}w_i(\;y_i,_j \; \ln(p_i,_j)\;)\], \[\frac{1}{c}\sum_{j=1}^{c} \text{AUC}(j, rest_j)\], \[\frac{1}{\sum_{j=1}^{c} p(j)} \sum_{j=1}^{c} p(j) \text{AUC}(j, rest_j)\], \[\frac{2}{c}\sum_{j=1}^{c}\sum_{k \neq j}^{c} \frac{1}{2}(\text{AUC}(j | k) + \text{AUC}(k | j))\], \[\frac{2}{\sum_{j=1}^{c}\sum_{k \neq j}^c p(j \cup k)}\sum_{j=1}^{c}\sum_{k \neq j}^c p(j \cup k)\frac{1}{2}(\text{AUC}(j | k) + \text{AUC}(k | j))\], \[\frac{1}{c}\sum_{j=1}^{c} \text{AUCPR}(j, rest_j)\], \[\frac{1}{\sum_{j=1}^{c} p(j)} \sum_{j=1}^{c} p(j) \text{AUCPR}(j, rest_j)\], \[\frac{2}{c}\sum_{j=1}^{c}\sum_{k \neq j}^{c} \frac{1}{2}(\text{AUCPR}(j | k) + \text{AUCPR}(k | j))\], \[\frac{2}{\sum_{j=1}^{c}\sum_{k \neq j}^c p(j \cup k)}\sum_{j=1}^{c}\sum_{k \neq j}^c p(j \cup k)\frac{1}{2}(\text{AUCPR}(j | k) + \text{AUCPR}(k | j))\], \[KS = \;\sup_{x}|\;F_1,_n(x) - F_2,_m(x)\;|\], \[{PD}(X_j, g) = {E}_{X_{(-j)}} \big{[}g(X_j, X_{(-j)})\big{]} = \frac{1}{N}\sum_{i = 0}^{N-1}g(x_j, \mathbf{x}_{(-j)}^{(i)})\], "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv", # set the predictor names and the response column name, # 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. One of either col_pairs_2dpdp or cols must be specified. --decimalplaces (or -d): Allows you to control the number of decimal points shown for numbers. Vous pouvez vous désinscrire de ces e-mails à tout moment. It is not supported in R. To convert a H2O MOJO into the ONNX format, use the onnxmltools python package. Coefficients can be positive (orange) or negative (blue). H2O-3 supports TreeSHAP for DRF, GBM, and XGBoost. If you predict a product will need to be restocked when it actually doesn’t, you incur cost by having purchased more inventory than you actually need. En créant cette alerte Emploi, vous acceptez les Conditions d’utilisation et la Politique de confidentialité de LinkedIn. AUC with class \(j\) as the positive class and rest classes \(rest_j\) as the The metric is composed of these outputs: One class versus one class (OVO) AUCs - calculated for all pairwise combination of classes ((number of classes × number of classes / 2) - number of classes results), One class versus rest classes (OVR) AUCs - calculated for all combination one class and rest of classes (number of classes results), Macro average OVR AUC - Uniformly weighted average of all OVO AUCs. nbins: The number of bins used. The Gini index is a well-established method to quantify the inequality among values of a frequency distribution, and can be used to measure the quality of a binary classifier. Ideally, a highly accurate ROC resembles the following example. # Import required packages for running SHAP commands, # Convert the H2OFrame to use with SHAP's visualization functions, # Expected values is the last returned column, # Summarize the effects of all the features. Getting a new observation from a JSON request and returning a prediction. The Kolmogorov-Smirnov (KS) metric represents the degree of separation between the positive (1) and negative (0) cumulative distribution functions for a binomial model. scaled between 0 and 1, use when target values are The efficiency gains are larger the bigger the size of the model. destination_key: A key reference to the created partial dependence tables in H2O. Using the previous example, run the following to retrieve the logloss value. H2O’s core code is … If the file already exists, it will be overridden. When a metric is sensitive to outliers, it means that it is important that the model predictions are never “very” wrong. Using the previous example, run the following to retrieve the MSE value. How do I communicate with a remote cluster using the REST API? The AUC calculation is disabled (set to NONE) by default. # example for Mac OsX if not already installed. Java developers should refer to the Javadoc for more information, including packages. Include the following contents. AUC with class \(j\) as the positive class and class \(k\) as the The R2 value represents the degree that the predicted value and the actual value move in unison. Again, this may slow down the MOJO due to added computation. Obtenez le cours le plus récent de H2O Innovation Inc. (HEO), ainsi que les nouvelles, les opérations, les graphiques, les activités d’initiés et les recommandations d’analystes les plus récentes. ATTENTION une petite erreur de saisie pour la formule de l'octane ce n'est MOJOs can be used in Java applications and they can also be used in R/Python to generate predictions for data stored in an in-memory R/Python data frame or in a CSV file. The h2o-genmodel.jar file is required when POJO/MOJO models are deployed to production. MOJOs are built in much the same way as POJOs. Virginie has 11 jobs listed on their profile. MSE takes the distances from the points to the regression line (these distances are the “errors”) and squaring them to remove any negative signs. Setting the absolute_mcc parameter sets the threshold for the model’s confusion matrix to a value that generates the highest Matthews Correlation Coefficient. For regression problems, predicted regression targets are compared against testing targets and typical error metrics. and its affiliates. Installée à PARIS 17 (75017), elle était spécialisée dans le secteur d'activité de la production … --tree: Optionally specify the tree number to print. Local Business. Using the previous example, run the following to retrieve the R2 value. Macro average OVO AUCPR - Uniformly weighted average of all OVO AUCPRs. Using the previous example, run the following to retrieve the F1 value. If your use case will use the probabilities, you will want to select a metric that evaluates the model’s performance based on the predicted probability. Download model pieces in a new terminal window. Logloss can be any value greater than or equal to 0, with 0 meaning that the model correctly assigns a probability of 0% or 100%. We have one prediction that was 30 days off. Detailed metrics per each group can be found in the gains-lift table. Notice that this is a paid AMI. negative class and \(p(j \cup k)\) is prevalence of class \(j\) and class \(k\) (sum of positives of both classes). For example, if you have a use case where 99% of the records have Class = No, then a model that always predicts No will have 99% accuracy. Multiclass problems require an additional targets parameter. If you train a model with the train data and validation data and also set the nfolds parameter, the Max F1 threshold from the validation data model metrics is used. When deciding which metric to use in a classification problem some main questions to ask are: Do you want the metric to evaluate the predicted probabilities or the classes that those probabilities can be converted to? where \(c\) is the number of classes and \(\text{AUCPR}(j, rest_j)\) is the Multiclass Classification: Prediction class labels are based on the class with the highest predicted probability. # This requires that graphviz is installed. The AUCPR calculation is disabled (set to NONE) by default. To view a specific threshold, select a value from the drop-down Threshold list. H2O PRODUCTIONS is a motion pictures and film company based out of 50 RUE MARCEL DASSAULT, BOULOGNE BILLANCOURT, France. "http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv", "Label (aka prediction) is flight departure delayed: ", "http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip", "http://:8080/model?type=1&row=2000,2000", Saving, Loading, Downloading, and Uploading Models, https://github.com/h2oai/h2o-3/tree/master/h2o-genmodel/src/main/java/hex/genmodel/easy/prediction. You can see this reflected in the behavior of the metrics: MSE and RMSE. The output is a string respresentation of the MOJO in either JSON or dot format. If you enable include_na, then the returned length will be nbins+1. # retrieve the gini value for the performance object: # retrieve the gini value for both the training and validation data: # retrieve the gini coefficient for both the training and validation data: # retrieve the mcc value for the performance object: # retrieve the mcc for the performance object: # retrieve the mcc for both the training and validation data: # retrieve the F1 value for the performance object: # retrieve the F1 coefficient for the performance object: # retrieve the F1 coefficient for both the training and validation data: # retrieve the F0.5 value for the performance object: # retrieve the F2 value for the performance object: # retrieve the F2 coefficient for the performance object: # retrieve the F2 coefficient for both the training and validation data: # retrieve the Accuracy value for the performance object: # retrieve the accuracy coefficient for the performance object: # retrieve the accuracy coefficient for both the training and validation data: # retrieve the logloss value for the performance object: # retrieve the logloss value for both the training and validation data: # retrieve the logloss for the performance object: # retrieve the logloss for both the training and validation data: # retrieve the AUC for the performance object: # retrieve the AUC for both the training and validation data: # retrieve the AUCPR for the performance object: # retrieve the AUCPR for both the training and validation data: # build a new model using gainslift_bins: # set the predictors and response columns: # split the data into training and validation sets: # build and train the model using the misclassification stopping metric: # import H2OGradientBoostingEstimator and the airlines dataset: # build and train the model using the lift_top_group stopping metric: # build and train the model using the lifttopgroup stopping metric: #split the data into training and validation sets: # build and train the model using the deviance stopping metric: # import H2OGradientBoostingEstimator and the cars dataset: # build and train the model using the mean_per_class_error stopping metric: # build and train the model using the meanperclasserror stopping metric: # set the predictors columns, response column, and distribution type: "http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip".

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