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get started with trainable classifiers - microsoft 365 compliance | microsoft docs

get started with trainable classifiers - microsoft 365 compliance | microsoft docs

A Microsoft 365 trainable classifier is a tool you can train to recognize various types of content by giving it samples to look at. Once trained, you can use it to identify item for application of Office sensitivity labels, Communications compliance policies, and retention label policies.

Creating a custom trainable classifier first involves giving it samples that are human picked and positively match the category. Then, after it has processed those, you test the classifiers ability to predict by giving it a mix of positive and negative samples. This article shows you how to create and train a custom classifier and how to improve the performance of custom trainable classifiers and pre-trained classifiers over their lifetime through retraining.

Opt-in is required the first time for trainable classifiers. It takes twelve days for Microsoft 365 to complete a baseline evaluation of your organizations content. Contact your global administrator to kick off the opt-in process.

When you want a trainable classifier to independently and accurately identify an item as being in particular category of content, you first have to present it with many samples of the type of content that are in the category. This feeding of samples to the trainable classifier is known as seeding. Seed content is selected by a human and is judged to represent the category of content.

You need to have at least 50 positive samples and as many as 500. The trainable classifier will process up to the 500 most recent created samples (by file created date/time stamp). The more samples you provide, the more accurate the predictions the classifier will make.

Once the trainable classifier has processed enough positive samples to build a prediction model, you need to test the predictions it makes to see if the classifier can correctly distinguish between items that match the category and items that don't. You do this by selecting another, hopefully larger, set of human picked content that consists of samples that should fall into the category and samples that won't. You should test with different data than the initial seed data you first provided. Once it processes those, you manually go through the results and verify whether each prediction is correct, incorrect, or you aren't sure. The trainable classifier uses this feedback to improve its prediction model.

Collect between 50-500 seed content items. These must be only samples that strongly represent the type of content you want the trainable classifier to positively identify as being in the classification category. See, Default crawled file name extensions and parsed file types in SharePoint Server for the supported file types.

Make sure the items in your seed set are strong examples of the category. The trainable classifier initially builds its model based on what you seed it with. The classifier assumes all seed samples are strong positives and has no way of knowing if a sample is a weak or negative match to the category.

Within 24 hours the trainable classifier will process the seed data and build a prediction model. The classifier status is In progress while it processes the seed data. When the classifier is finished processing the seed data, the status changes to Need test items.

Collect at least 200 test content items (10,000 max) for best results. These should be a mix of items that are strong positives, strong negatives and some that are a little less obvious in their nature. See, Default crawled file name extensions and parsed file types in SharePoint Server for the supported file types.

When the trainable classifier is done processing your test files, the status on the details page will change to Ready to review. If you need to increase the test sample size, choose Add items to test and allow the trainable classifier to process the additional items.

Microsoft 365 will present 30 items at a time. Review them and in the We predict this item is "Relevant". Do you agree? box choose either Yes or No or Not sure, skip to next item. Model accuracy is automatically updated after every 30 items.

machine learning classifiers - the algorithms & how they work

machine learning classifiers - the algorithms & how they work

A classifier in machine learning is an algorithm that automatically orders or categorizes data into one or more of a set of classes. One of the most common examples is an email classifier that scans emails to filter them by class label: Spam or Not Spam.

A classifier is the algorithm itself the rules used by machines to classify data. A classification model, on the other hand, is the end result of your classifiers machine learning. The model is trained using the classifier, so that the model, ultimately, classifies your data.

There are both supervised and unsupervised classifiers. Unsupervised machine learning classifiers are fed only unlabeled datasets, which they classify according to pattern recognition or structures and anomalies in the data. Supervised and semi-supervised classifiers are fed training datasets, from which they learn to classify data according to predetermined categories.

Sentiment analysis is an example of supervised machine learning where classifiers are trained to analyze text for opinion polarity and output the text into the class: Positive, Neutral, or Negative. Try out this pre-trained sentiment analysis model to see how it works.

Machine learning classifiers are used to automatically analyze customer comments (like the above) from social media, emails, online reviews, etc., to find out what customers are saying about your brand.

Other text analysis techniques, like topic classification, can automatically sort through customer service tickets or NPS surveys, categorize them by topic (Pricing, Features, Support, etc.), and route them to the correct department or employee.

SaaS text analysis platforms, like MonkeyLearn, give easy access to powerful classification algorithms, allowing you to custom-build classification models to your needs and criteria, usually in just a few steps.

Machine learning classifiers go beyond simple data mapping, allowing users to constantly update models with new learning data and tailor them to changing needs. Self-driving cars, for example, use classification algorithms to input image data to a category; whether its a stop sign, a pedestrian, or another car, constantly learning and improving over time.

A decision tree is a supervised machine learning classification algorithm used to build models like the structure of a tree. It classifies data into finer and finer categories: from tree trunk, to branches, to leaves. It uses the if-then rule of mathematics to create sub-categories that fit into broader categories and allows for precise, organic categorization.

Naive Bayes is a family of probabilistic algorithms that calculate the possibility that any given data point may fall into one or more of a group of categories (or not). In text analysis, Naive Bayes is used to categorize customer comments, news articles, emails, etc., into subjects, topics, or tags to organize them according to predetermined criteria, like this:

K-nearest neighbors (k-NN) is a pattern recognition algorithm that stores and learns from training data points by calculating how they correspond to other data in n-dimensional space. K-NN aims to find the k closest related data points in future, unseen data.

In text analysis, k-NN would place a given word or phrase within a predetermined category by calculating its nearest neighbor: k is decided by a plurality vote of its neighbors. If k = 1, it would be tagged into the class nearest 1.

Take a look at this visual representation to understand how SVM algorithms work. We have two tags: red and blue, with two data features: X and Y, and we train our classifier to output an X/Y coordinate as either red or blue.

The SVM assigns a hyperplane that best separates (distinguishes between) the tags. In two dimensions this is simply a straight line. Blue tags fall on one side of the hyperplane and red on the other. In sentiment analysis these tags would be Positive and Negative.

SVM algorithms make excellent classifiers because, the more complex the data, the more accurate the prediction will be. Imagine the above as a 3-dimensional output, with a Z-axis added, so it becomes a circle.

Artificial neural networks are designed to work much like the human brain does. They connect problem-solving processes in a chain of events, so that once one algorithm or process has solved a problem, the next algorithm (or link in the chain) is activated.

Artificial neural networks or deep learning models require vast amounts of training data because their processes are highly advanced, but once they have been properly trained, they can perform beyond other, individual, algorithms.

There are a variety of artificial neural networks, including convolutional, recurrent, feed-forward, etc., and the machine learning architecture best suited to your needs depends on the problem youre aiming to solve.

Classification algorithms enable the automation of machine learning tasks that were unthinkable just a few years ago. And, better yet, they allow you to train AI models to the needs, language, and criteria of your business, performing much faster and with a greater level of accuracy than humans ever could.

MonkeyLearn is a machine learning text analysis platform that harnesses the power of machine learning classifiers with an exceedingly user-friendly interface, so you can streamline processes and get the most out of your text data for valuable insights.

adding classifiers to a crawler - aws glue

adding classifiers to a crawler - aws glue

A classifier reads the data in a data store. If it recognizes the format of the data, it generates a schema. The classifier also returns a certainty number to indicate how certain the format recognition was.

AWS Glue provides a set of built-in classifiers, but you can also create custom classifiers. AWS Glue invokes custom classifiers first, in the order that you specify in your crawler definition. Depending on the results that are returned from custom classifiers, AWS Glue might also invoke built-in classifiers. If a classifier returns certainty=1.0 during processing, it indicates that it's 100 percent certain that it can create the correct schema. AWS Glue then uses the output of that classifier.

If no classifier returns certainty=1.0, AWS Glue uses the output of the classifier that has the highest certainty. If no classifier returns a certainty greater than 0.0, AWS Glue returns the default classification string of UNKNOWN.

You use classifiers when you crawl a data store to define metadata tables in the AWS Glue Data Catalog. You can set up your crawler with an ordered set of classifiers. When the crawler invokes a classifier, the classifier determines whether the data is recognized. If the classifier can't recognize the data or is not 100 percent certain, the crawler invokes the next classifier in the list to determine whether it can recognize the data.

For more information about creating a classifier using the AWS Glue console, see Working with Classifiers on the AWS Glue Console.

The output of a classifier includes a string that indicates the file's classification or format (for example, json) and the schema of the file. For custom classifiers, you define the logic for creating the schema based on the type of classifier. Classifier types include defining schemas based on grok patterns, XML tags, and JSON paths.

If you change a classifier definition, any data that was previously crawled using the classifier is not reclassified. A crawler keeps track of previously crawled data. New data is classified with the updated classifier, which might result in an updated schema. If the schema of your data has evolved, update the classifier to account for any schema changes when your crawler runs. To reclassify data to correct an incorrect classifier, create a new crawler with the updated classifier.

If your data format is recognized by one of the built-in classifiers, you don't need to create a custom classifier.

If AWS Glue doesn't find a custom classifier that fits the input data format with 100 percent certainty, it invokes the built-in classifiers in the order shown in the following table. The built-in classifiers return a result to indicate whether the format matches (certainty=1.0) or does not match (certainty=0.0). The first classifier that has certainty=1.0 provides the classification string and schema for a metadata table in your Data Catalog.

For information about creating a custom XML classifier to specify rows in the document, see Writing XML Custom Classifiers.

ZIP (supported for archives containing only a single file). Note that Zip is not well-supported in other services (because of the archive).

The built-in CSV classifier parses CSV file contents to determine the schema for an AWS Glue table. This classifier checks for the following delimiters:

To be classified as CSV, the table schema must have at least two columns and two rows of data. The CSV classifier uses a number of heuristics to determine whether a header is present in a given file. If the classifier can't determine a header from the first row of data, column headers are displayed as col1, col2, col3, and so on. The built-in CSV classifier determines whether to infer a header by evaluating the following characteristics of the file:

Except for the last column, every column in a potential header has content that is fewer than 150 characters. To allow for a trailing delimiter, the last column can be empty throughout the file.

The header row must be sufficiently different from the data rows. To determine this, one or more of the rows must parse as other than STRING type. If all columns are of type STRING, then the first row of data is not sufficiently different from subsequent rows to be used as the header.

If the built-in CSV classifier does not create your AWS Glue table as you want, you might be able to use one of the following alternatives:

Change the column names in the Data Catalog, set the SchemaChangePolicy to LOG, and set the partition output configuration to InheritFromTable for future crawler runs.

The built-in CSV classifier creates tables referencing the LazySimpleSerDe as the serialization library, which is a good choice for type inference. However, if the CSV data contains quoted strings, edit the table definition and change the SerDe library to OpenCSVSerDe. Adjust any inferred types to STRING, set the SchemaChangePolicy to LOG, and set the partitions output configuration to InheritFromTable for future crawler runs. For more information about SerDe libraries, see SerDe Reference in the Amazon Athena User Guide.

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classifier | definition of classifier by merriam-webster

classifier | definition of classifier by merriam-webster

These example sentences are selected automatically from various online news sources to reflect current usage of the word 'classifier.' Views expressed in the examples do not represent the opinion of Merriam-Webster or its editors. Send us feedback.

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