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The rate of the water flowing in the channel was 2 miles per hour. The total time it took them to kayak up and back was 3 hours and 40 minutes. Assuming they were math upstream downstream problems query their kayak at a fairly consistent rate find the rate Evelyn and Meridith were paddling?

The downstream rate will have an overall rate faster than the boat in still water since the flow of the water speeds them up:. The total time ws 3 hours and 40 min.

We need this in terms of hours so the minutes become a fraction of an hour to get:. What's left is a system of two equations and two unknowns. I'll start by solving the first equation for t in terms of b:. This is what the question asked. If we wanted to know the time we could solve for "t" and "T", but we've not been asked math upstream downstream problems query do that, so I'll leave this at.

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Biden says Rangers allowing full crowds is a 'mistake'. COVID 'breakthrough cases' are 'extremely unlikely'. Gaetz case takes bizarre turn involving ex-FBI agent. Evelyn and Meridith decided to kayak 1 mile up and then back in the Humboldt channel. Answer Save. Alvin Lv 4. Favorite Answer. Pramod Kumar Lv 7.

Daniel H Lv 5. Total time 3. Still have questions? Get your answers by asking .

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Identify root cause analysis of data inaccuracies and drive necessary action plans for data cleanse that support data integrity Develop data integrity processes, that clearly identifies roles, responsibilities and accountability for those working with HR data Work closely with key stakeholders to understand reporting requirements and build value add reporting calendar for the Executive ensuring delivery is on time and to quality Support EBETT on data interpretation, creating visualizations to explain complex concepts to support business commercial decisions Create a network of data and reporting leads in regions to learn and share best practice Contribute to EY firm-wide programs representing the Executive, taking a consultative approach supporting solution design aiming for fit for purpose delivery e.

People data reporting tool PDR Gain in-depth knowledge of local country policies and processes around data management of the Executive populations Build and own management reporting processes and procedures in conjunction with recruiting, learning, HR, mobility and enablement functions in EBETT Provide expert input to the data and reporting standards ensuring sound methodologies are applied and valued conclusions are drawn from the relevant management reports Work in collaboration with EBETT to develop new reports, enhance existing reports or retire reports that no longer support the Executive talent priorities Train and educate key stakeholders EBETT and Senior Leadership in the Executive on the methods and value of reporting.

Relevant experience and knowledge of financial and management reporting Strong technical skills with knowledge of Access and SQ Able to drive projects and process improvements Ability to establish strong internal relationships across businesses and functions and to work in partnership with multiple parties to achieve shared goals Ability to work in a dynamic and fast-paced environment with tight timeframes Strong knowledge of MS Access and MS Excel and SQL VBA skills would be an advantage.

Process centralized fund accounting transactions including trades processing Capstock reconciliation and processing Participate in internal and external audit requests years experience in Fund Accounting, Pricing, Client reporting environment or related field.

Develop CRM-related capital plan documentation for CCAR, including related GMS and 9Q methodology overviews, business overviews and results narratives, and support and coordination with the analytics team on technical methodology documentation.

Coordinate with modeling teams to ensure timely and accurate delivery of forecasted results Perform regular CRM BAU roles such as risk appetite setting, and internal stress testing on key portfolio sensitivities e. Management, creation, and enhancement of standard deliverables including headcount, diversity, turnover, promotion, span of control, management level, and staffing reporting Serve as the point of contact for GBAM Strategy and GBAM Finance for Business Reviews, Financial Forecasts, and other meetings � i.

No supervisory responsibilities. OMS, risk, etc. With limited supervision, debugs complex software products through the use of systematic tests to develop, apply and maintain quality standards for company products Maintains and creates automated scripts for projects Responsible for the health and state of regression tests Responsible for consolidation of test results, procedures and application specifications Develops and executes software test plans Analyzes and writes test standards and procedures Maintains documentation of test results to assist in debugging and modification of software Analyzes test results to ensure existing functionality and recommends corrective action Consults with development engineers and business analysts to resolve problems Assists with the designing, coding and execution of automated test scripts.

Data mining experience, information extraction or statistical modeling. Microsoft Access and Excel SAS Tableau 2 years related experience, healthcare a plus generally required Data Analysis and Data Manipulation Follows established schedules, CMS calendars, reporting and requirements Properly documents work Makes decisions with guidance Maintains assigned financial reports.

Works with the data owner to understand and describe the data in the data catalog Discuss data requirements with data sources Detail and support the development and testing of the Data Platform capabilities Work with the development teams to define the data flows within the Data Platform Build and maintain client relationship Data Mapping and interface definition Report Design Data Modeling and understanding of the data model principals Data Warehouse and Metadata Management Data Governance and Business terms glossary experience is an added value User Acceptance test case follow-up and support Good SQL skills is always an added value.

Tech, MCA. Communicates effectively and clearly on a daily, weekly, monthly basis with outside vendors to provide exceptional customer service regarding routine requests Responds promptly to vendor requests information, data, letter generation, etc. Assume cross-functional leadership of a team of skilled employees Use your hands-on experience with master data to set the direction Nurture the skill-set and the mentality of your team to establish a culture focusing on the end product and the users from an outside-in perspective Improve our processes by documenting and collecting inputs in close collaboration with process owners and continuous improvement partners Validate data quality and create a set-up reporting across multiple locations globally You have multiple years of experience from a similar position, where you have gained a profound understanding of how best to handle vast amounts of master data You are an experienced leader, who has led larger teams and preferably also other leaders You have international experience from a multinational company as a business partner You are highly analytical and a strong communicator - both numerically and linguistically You are a SAP Super User and an Excel expert.

Highly-motivated, self-directed, and able to effectively work with limited oversight Strong interpersonal communication skills verbal and written , including the ability to effectively articulate Math Upstream Downstream Problems For business solutions Substantial experience in the operation and analysis of relational databases and standards, as well as data retrieval and organization methodologies Identify master data management needs and implement standard work and process to facilitate the necessary MDM solutions as pertaining to the database solutions Develop and leverage business knowledge and creativity to visually report the data, maintain substance, and provide solutions.

ISO and and information security industry standards e. Alternately years of experience in contract data management will be preferred. Collect business requirements from Sales, Project Management, and Production Develop and plan to ensure a high level of information accuracy and quality for all facets of the job, including change control and validations Manipulates, extracts, warehouses data in multiple formats Apply advanced Excel functions such as VLOOKUP, Macros, Pivot Tables, Query, etc.

Support business change in the organisation e. Manages staff. Provides guidance, training and motivation as necessary to develop this staff. Hires, evaluates and counsels personnel. Follows corporate disciplinary procedures per established Vanguard standards as required.

Ensures all reports and analyses are accurate, clear, timely, concise and user-friendly Serves as gatekeeper for FAS ad-hoc and strategic reporting requests. Establishes, communicates, and enforces an intake process to create a great client experience. Negotiates with clients and recommends prioritization of reporting efforts. Develops and maintains a reporting service model to ensure client needs are being met.

Responsible for monitoring crew to adhere to best practices for report creation and information security standards Establishes easily accessible reporting suite and documents and maintains metrics definitions. Keeps reporting suite current to business needs and provides maintenance to metric definitions related to business changes.

Works with IT and Business partners to support information flow for new business ideas, business and industry changes. Consistently seeks ways to improve reporting processes to add value to clients. And overall; collaborates with the Data Governance and Quality team to identify opportunities to continually evolve the governance and quality of the FAS data sets, manages the team of Analysts to adhere to the data governance policies and processes, and oversee 3rd party data implementation and integration efforts Leads the development and execution of business change management and communication plans, or ensures integration of these activities with other major programs as appropriate.

Ensures highly effective communications and business readiness regarding project status and issues occur regularly to key stakeholders in a variety of forums sponsor, division meetings, TRGs, etc. Establishes metrics i. Works with Sales and management sponsors to support reporting initiatives Represents FAS data reporting interests in corporate or cross-divisional initiatives.

This error in Azure Machine Learning occurs if the target column as selected in the module parameters is not of the valid data-type, contained all missing values, or was not categorical as expected.

Make sure it does not have all missing values. If the module is expecting target column to be categorical, make sure that there are more than one distinct values in the target column. This error in Azure Machine Learning occurs when the module requires a label column and the dataset does not have a label column. For example, evaluation of a scored dataset usually requires that a label column is present to compute accuracy metrics.

It can also happen that a label column is present in the dataset, but not detected correctly by Azure Machine Learning. This error in Azure Machine Learning occurs if the input to the evaluate model does not contain valid score columns. For example, the user attempts to evaluate a dataset before it was scored with a correct trained model, or the score column was explicitly dropped upstream.

This exception also occurs if the score columns on the two datasets are incompatible. For example, you might be trying to compare the accuracy of a linear regressor with that of a binary classifier.

Resolution: Revisit the input to the evaluate model and examine if it contains one or more score columns. If not, the dataset was not scored or the score columns were dropped in an upstream module. This error in Azure Machine Learning occurs if multiple columns have the same name. One way you may receive this error is if the dataset does not have a header row and column names are automatically assigned: Col0, Col1, etc. Resolution: If columns have same name, insert a Edit Metadata module between the input dataset and the module.

Use the column selector in Edit Metadata to select columns to rename, typing the new names into the New column names textbox. Resolution: There is no specific resolution. However, you can check for conditions such as the following:. If you are renaming columns, make sure that each list the input columns and the list of new names has the same number of items.

If you are joining two datasets that have multiple columns, make sure that the key columns have the same data type, and select the option Allow duplicates and preserve column order in selection. Exception occurs in the case when column set contains duplicated column names and it is not allowed. This error in Azure Machine Learning occurs when column names are duplicated; that is, not unique.

Resolution: If any columns have same name, add an instance of Edit Metadata between the input dataset and the module raising the error. Use the Column Selector in Edit Metadata to select columns to rename, and type the new columns names into the New column names textbox. If you are renaming multiple columns, ensure that the values you type in the New column names are unique. You will receive this error if any of the following conditions are true:, or.

Resolution: Revisit the module and verify the format of the URI. This exception in Azure Machine Learning occurs when it is not possible to download a file. You will receive this exception when an attempted read from an HTTP source has failed after three 3 retry attempts.

This error in Azure Machine Learning occurs if the number of columns selected is less than needed. You will receive this error if the minimum required number of columns are not selected. Resolution: Add additional columns to the column selection by using the Column Selector. This error in Azure Machine Learning occurs if the argument is infinite. You will receive this error if the argument is either double.

NegativeInfinity or double. This error in Azure Machine Learning occurs in recommendation if a user-item pair has more than one rating value. This error in Azure Machine Learning occurs you are trying to use a recommendation model for scoring but a feature vector cannot be found. The Matchbox recommender has certain requirements that must be met when using either item features or user features.

This error indicates that a feature vector is missing for a user or item that you provided as input. You must ensure that a vector of features is available in the data for each user or item. For example, if you trained a recommendation model using features such as the user's age, location, or income, but now want to create scores for new users who were not seen during training, you must provide some equivalent set of features namely, age, location, and income values for the new users in order to make appropriate predictions for them.

If you do not have any features for these users, consider feature engineering to generate appropriate features. For example, if you do not have individual user age or income values, you might generate approximate values to use for a group of users. When you are scoring from a recommendation mode, you can use item or user features only if you previously used item or user features during training.

For more information, see Score Matchbox Recommender. For general information about how the Matchbox recommendation algorithm works, and how to prepare a dataset of item features or user features, see Train Matchbox Recommender.

Resolution not applicable to your case? You are welcome to send feedback on this article and provide information about the scenario, including the module and the number of rows in the column. We will use this information to provide more detailed troubleshooting steps in the future.

This error in Azure Machine Learning occurs if more than one column is selected to be the new label column. Most supervised learning algorithms require a single column to be marked as the target or label. This error in Azure Machine Learning occurs if the number of elements expected should be an exact value, but is not.

You will receive this error if the number of elements is not equal to the valid expected value. Resolution: This error is caused by many conditions and there is no specific remedy. The following table contains generic messages for this error, which are followed by a specific description of the condition. If no details are available, send feedback and provide information about the modules that generated the error and related conditions.

Resolution: Replace the deprecated module with a supported one. See the module output log for the information about which module to use instead. Resolution: Replace the deprecated module with a set of supported ones.

This information should be visible in the module output log. This error in Azure Machine Learning occurs when it is not possible to convert column to the specified type. You will receive this error if a module requires a particular data type, such as datetime, text, a floating point number, or integer, but it is not possible to convert an existing column to the required type.

For example, you might select a column and try to convert it to a numeric data type for use in a math operation, and get this error if the column contained invalid data. Another reason you might get this error if you try to use a column containing floating point numbers or many unique values as a categorical column. Resolution unclear, or not applicable to your case?

You are welcome to send feedback on this article and provide information about the scenario, including the module and the data type of the column. Exception occurs when it is not possible to derive element type of column from the existing values. This error in Azure Machine Learning occurs when it is not possible to infer the type of a column or columns in a dataset.

This typically happens when concatenating two or more datasets with different element types. If Azure Machine Learning is unable to determine a common type that is able to represent all the values in a column or columns without loss of information, it will generate this error. Resolution: Ensure that all values in a given column in both datasets being combined are either of the same type numeric, Boolean, categorical, string, date, etc.

Exception occurs when it is not Math Upstream Downstream Problems Dictionary possible to create a column because of mixed element types in the source. This error in Azure Machine Learning is produced when the element types of two datasets being combined are different.

Resolution: Ensure that all values in a given column in both datasets being combined are of the same type numeric, Boolean, categorical, string, date, etc. This error in Azure Machine Learning occurs when it is not possible to create a directory on the specified path. You will receive this error if any part of the path to the output directory for a Hive Query is incorrect or inaccessible.

Resolution: Revisit the module and verify that the directory path is correctly formatted and that it is accessible with the current credentials. Exception occurs if number of feature columns in some of the datasets passed to the module is too small. This error in Azure Machine Learning occurs if the input dataset to training does not contain the minimum number of columns required by the algorithm.

Typically either the dataset is empty or only contains training columns. Resolution: Revisit the input dataset to make sure there one or more additional columns apart from the label column. This error in Azure Machine Learning occurs when it is not possible to open a file for read or write. You might receive this error for these reasons:.

Use the Azure classic portal or an Azure storage tool to verify that you have permission to access the file. If you are trying to read an image file, make sure that it meets the requirements for image files in terms of size, number of pixels, and so forth. For more information, see Import Images. This error in Azure Machine Learning occurs when it is not possible to parse a file.

You will receive this error if the file format selected in the Import Data module does not match the actual format of the file, or if the file contains an unrecognizable character. Resolution: Revisit the module and correct the file format selection if it does not match the format of the file.

If possible, inspect the file to confirm that it does not contain any illegal characters. Resolution: This error in Azure Machine Learning is unused and will be deprecated.

This error in Azure Machine Learning occurs if the key used to access the Azure storage account is incorrect. For example, you might see this error if the Azure storage key was truncated when copied and pasted, or if the wrong key was used. For more information about how to get the key for an Azure storage account, see View, copy, and regenerate storage access keys. Resolution: Revisit the module and verify that the Azure storage key is correct for the account; copy the key again from the Azure classic portal if necessary.

Exception occurs in the case when there are no user features or items for matchbox recommendations. This error in Azure Machine Learning appears if you try to call a module that has been deprecated. This error in Azure Machine Learning occurs when you are choosing columns for an operation that requires the column be of a particular data type.

This error can also happen if the column is the correct data type, but the module you are using requires that the column also be marked as a feature, label, or categorical column. For example, the Convert to Indicator Values module requires that columns be categorical, and will raise this error if you select a feature column or label column.

Review the help topic for the module in which you made the column selection, to determine if there are specific requirements for data type or column usage. Use Edit Metadata to change the column type for the duration of this operation.

Be sure to change the column type back to its original value, using another instance of Edit Metadata , if you need it for downstream operations. This exception occurs when you are using the Export Data module or other module to save results of an experiment in Azure Machine Learning to Azure blob storage, but you attempt to create a file or blob that already exists. You will receive this error only if you previously set the property Azure blob storage write mode to Error.

By design, this module raises an error if you attempt to write a dataset to a blob that already exists. This error in Azure Machine Learning occurs if the dataset does not contain the expected label column. This exception can also occur when the label column provided does not match the data or datatype expected by the learner, or has the wrong values.

For example, this exception is produced when using a real-valued label column when training a binary classifier. Resolution: The resolution depends on the learner or trainer that you are using, and the data types of the columns in your dataset. First, verify the requirements of the machine learning algorithm or training module. Revisit the input dataset. Verify that the column you expect to be treated as the label has the right data type for the model you are creating.

If necessary, add the Edit Metadata module and ensure that the label column is marked as a label. This error in Azure Machine Learning occurs if a column index specified when using the Column Selector cannot be parsed.

You will receive this error when the column index is in an invalid format that cannot be parsed. This error in Azure Machine Learning occurs when an out-of-range column range is specified in the Column Selector.

You will receive this error if the column range in the column picker does not correspond to the columns in the dataset. Resolution: Modify the column range in the column picker to correspond to the columns in the dataset. Exception occurs when attempting to add a row to a DataTable that has a different number of columns than the table. This error in Azure Machine Learning occurs when you attempt to add a row to a dataset that has a different number of columns than the dataset.

You will receive this error if the row that is being added to the dataset has a different number of columns from the input dataset. The row cannot be appended to the dataset if the number of columns is different.

Resolution: Modify the input dataset to have the same number of columns as the row added, or modify the row added to have the same number of columns as the dataset. This error in Azure Machine Learning is produced when evaluation metrics for two different scored datasets cannot be compared.

In this case, it is not possible to compare the effectiveness of the models used to produce the two scored datasets. Resolution: Verify that the scored results are produced by the same kind of machine learning model binary classification, regression, multi-class classification, recommendation, clustering, anomaly detection, etc.

All models that you compare must have the same learner type. This error occurs when you have provided an R script in one of the R language modules in Azure Machine Learning, and the R code contains internal syntax errors.

The exception can also occur if you provide the wrong inputs to the R script. The error can also occur if the script is too large to execute in the workspace. The maximum script size for the Execute R Script module is 1, lines or 32 KB of work space, whichever is lesser. These topics contains examples of R code that you can use, as well as links to experiments in the Cortana Intelligence Gallery that use R script.

This error in Azure Machine Learning occurs if the Azure storage account name or storage key is specified incorrectly. You will receive this error if you enter an incorrect account name or password for the storage account. This may occur if you manually enter the account name or password.

It may also occur if the account has been deleted. Resolution: Verify that the account name and password have been entered correctly, and that the account exists. This error in Azure Machine Learning occurs if the Azure blob name is specified incorrectly. You will receive the error if:. The fully qualified name of the blob specified for output in one of the Learning with Counts modules is greater than characters.

Only the container was specified as the source in a Import Data request when the format was Excel or CSV with encoding; concatenation of the contents of all blobs within a container is not allowed with these formats. Resolution: Revisit the module throwing the exception. Verify that the specified blob does exist in the container in the storage account and that permissions allow you to see the blob.

You will receive this message if Train Vowpal Wabbit Model encounters an error attempting to save either the model or the hash created when training the model. Both are saved to the same Azure storage account as the account containing the input file. Resolution: Revisit the module. Verify that the Azure account name, storage key, and container are correct and that the account has permission to write to the container.

This error in Azure Machine Learning occurs if a dataset has a different number of columns than expected. You will receive this error when the number of columns in the dataset are different from the number of columns that the module expects during execution. This error in Azure Machine Learning occurs if there are syntax errors in a Hive QL script, or if the Hive interpreter encounters an error while executing the query or script.

The error message from Hive is normally reported back in the Error Log so that you can take action based on the specific error. This error in Azure Machine Learning occurs if the specified SQL script has syntax problems, or if the columns or table specified in the script is not valid. You will receive this error if the SQL engine encounters any error while executing the query or script. The SQL error message is normally reported back in the Error Log so that you can take action based on the specific error.

Verify that the query works correctly outside of Azure ML by logging in to the database server directly and running the query. If there is a SQL generated message reported by the module exception, take action based on the reported error. For example, the error messages sometimes include specific guidance on the likely error:.

This error in Azure Machine Learning occurs when you attempt to access a non-existent Azure table. You will receive this error if you specify a table in Azure storage, which does not exist when reading from or writing to Azure Table Storage. This can happen if you mistype the name of the desired table, or you have a mismatch between the target name and the storage type.

For example, you intended to read from a table but entered the name of a blob instead. Resolution: Review the inputs to the module and verify the account name and password. You are writing to a table that has column names with illegal characters.

If you are trying to connect to an HDInsight cluster, verify that the target cluster is accessible with the supplied credentials. This error in Azure Machine Learning occurs when a connection times out. Resolution: Determine whether there are currently issues with slow connections to Azure storage or the internet. This error in Azure Machine Learning occurs when it is not possible to convert column to another type. You will receive this error if a module requires a particular type and it is not possible to convert the column to the new type.

Resolution: Modify the input dataset so that the column can be converted based on the inner exception.

Exception occurs when the Edit Metadata tries to convert a sparse column to categorical. This error in Azure Machine Learning occurs when the Edit Metadata tries to convert a sparse column to categorical. You will receive this error when trying to convert sparse columns to categorical with the Make categorical option.

Azure machine Learning does not support sparse categorical arrays, so the module will fail. Resolution: Make the column dense by using Convert to Dataset first Math Upstream Downstream Problems Rule or do not convert the column to categorical. This error in Azure Machine Learning occurs when you are trying to bin data using an unsupported method, or when the parameter combinations are invalid.

Error handling for this event was introduced in an earlier version of Azure Machine Learning that allowed more customization of binning methods. Currently all binning methods are based on a selection from a dropdown list, so technically it should no longer be possible to get this error. If you get this error when using the Group Data into Bins module, consider reporting the issue in the Azure Machine Learning forum , providing the data types, parameter settings, and the exact error message.

This error in Azure Machine Learning occurs if an invalid argument is passed in the specifications for a blob file destination or source. Resolution: In almost all modules that import or export data to and from Azure blob storage, parameter values controlling the write mode are assigned by using a dropdown list; therefore, it is not possible to pass an invalid value, and this error should not appear.

This error will be deprecated in a later release. You will receive this error if you attempt to connect to an HTTP source that redirects the browser to another page. For security reasons, redirecting websites are not allowed as data sources for Azure Machine Learning.

Resolution: If the website is a trusted website, enter the redirected URL directly. This error in Azure Machine Learning occurs if the Azure storage container name is specified incorrectly. You will receive this error if you have not specified both the container and the blob file name using the Path to blob beginning with container option when writing to Azure Blob Storage.

This error in Azure Machine Learning is produced when one or more of the columns consumed by the module contains all missing values. For example, if a module is computing aggregate statistics for each column, it cannot operate on a column containing no data.

In such cases, module execution is halted with this exception. Resolution: Revisit the input dataset and remove Math Upstream Downstream Problems Jp any columns that contain all missing values. Exception occurs in PCA module if number of dimensions to reduce to is equal to number of feature columns in input dataset, containing at least one sparse feature column. This error in Azure Machine Learning is produced if the following conditions are met: a the input dataset has at least one sparse column and b the final number of dimensions requested is the same as the number of input dimensions.

Resolution: Consider reducing the number of dimensions in the output to be fewer than the number of dimensions in the input. This is typical in applications of PCA. For more information, see Principal Component Analysis. This error in Azure Machine Learning occurs when a saved machine learning model or transform cannot be loaded by a newer version of the Azure Machine Learning runtime as a result of a breaking change.

Resolution: The training experiment that produced the model or transform must be rerun and the model or transform must be resaved. This error in Azure Machine Learning is produced when the dataset is incompatible with the learner being trained.

For example, the dataset might contain at least one missing value in each row, and as a result, the entire dataset would be skipped during training.

In other cases, some machine learning algorithms such as anomaly detection do not expect labels to be present and can throw this exception if labels are present in the dataset. Resolution: Consult the documentation of the learner being used to check requirements for the input dataset.

Examine the columns to see all required columns are present. Exception occurs when scores produced from an R Script are evaluated. This is currently unsupported. This error in Azure Machine Learning occurs if you try to use one of the modules for evaluating a model with output from an R script that contains scores. This error in Azure Machine Learning occurs when you are running custom script that contains syntax errors. This error in Azure Machine Learning occurs when you select a transformation based on a count table, but the selected transform is incompatible with the current data, or with the new count table.

Resolution: The module supports saving the counts and rules that make up the transformation in two different formats. If you are merging count tables, verify that both tables you intend to merge use the same format. In general, a count-based transform can only be applied to datasets that have the same schema as the dataset on which the transform was originally created.

For general information, see Learning with Counts. For requirements specific to creating and merging count-based features, see these topics:. Modify Count Table Parameters. This error in Azure Machine Learning occurs when you try to import an existing count table, but the table is incompatible with the current data, or with the new count table.

Resolution: There are different formats for saving the counts and rules that make up the transformation. If you are merging count tables, verify that both use the same format. Generally, a count-based transform can only be applied to datasets that have the same schema as the dataset on which the transform was originally created.

This error in Azure Machine Learning occurs when you try to use a different counting method than is supported for count-based featurization. Resolution: In general, counting methods are chosen from a dropdown list, so you should not see this error. Exception occurs when the specified number of classes is less than the actual number of classes in a dataset used for counting. This error in Azure Machine Learning occurs when you are creating a count table and the label column contains a different number of classes than you specified in the module parameters.

Resolution: Check your dataset and find out exactly how many distinct values possible classes there are in the label column. When you create the count table, you must specify at least this number of classes. When you create the count table, you cannot specify 0 or any number that is less than the actual number of classes in the label column.

This error in Azure Machine Learning occurs when you are using Export Data or another option to save data to an HDInsight cluster and the specified Hive table cannot be created.

Resolution: Check the Azure storage account name associated with the cluster and verify that you are using the same account in the module properties. This error in Azure Machine Learning occurs when building a custom module and the name property of the Language element in a custom module xml definition file has an invalid value.

Currently, the only valid value for this property is R. Resolution: Verify that the name property of the Language element in the custom module xml definition file is set to R. Save the file, update the custom module zip package, and try to add the custom module again.

This error in Azure Machine Learning occurs when one or more ports or parameters are assigned the same ID value in a custom module XML definition file. Resolution: Check that the ID values across all ports and parameters are unique. Save the xml file, update the custom module zip package, and try to add the custom module again.

This error in Azure Machine Learning occurs when you are importing a zipped package with the. Resolution: Make sure the selected file is a valid. If you get this error when importing datasets in compressed format, verify that all contained files use one of the supported file formats, and are in Unicode format.

For more information, see Unpack Zipped Datasets. Try readding the desired files to a new compressed zipped folder and try to add the custom module again. This error in Azure Machine Learning occurs when the custom module zip package does not contain any module definition.

These files need to reside in the root of the zip package for example, not within a subfolder. Resolution: Verify that one or more xml module definition files are in the root folder of the zip package by extracting it to a temporary folder on your disk drive.

Any xml files should be directly in the folder you extracted the zip package to. Make sure when you create the zip package that you do not select a folder that contains xml files to zip as this will create a sub folder within the zip package with the same name as the folder you selected to zip. This error in Azure Machine Learning is thrown when a custom module xml definition file references a script file in the Language element that does not exist in the zip package.

The script file path is defined in the sourceFile property of the Language element. The path to the source file is relative to the root of the zip package same location as the module xml definition files. If the script file is in a sub folder, the relative path to the script file must be specified. For instance, if all scripts were stored in a myScripts folder within the zip package, the Language element would have to add this path to the sourceFile property as below.

Resolution: Make sure that the value of the sourceFile property in the Language element of the custom module xml definition is correct and that the source file exists in the correct relative path in the zip package. This error in Azure Machine Learning is produced when the you create a custom module xml definition and the type of a parameter or argument in the definition does not match a supported type.

Resolution: Make sure that the type property of any Arg element in the custom module xml definition file is a supported type. This error in Azure Machine Learning is produced when the type of an input port in a custom module XML definition does not match a supported type.

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